From d3571e2f7e47e37cf9ab94b1a879e710756d0630 Mon Sep 17 00:00:00 2001
From: AyuavnGautam <91385710+AyuavnGautam@users.noreply.github.com>
Date: Wed, 1 Dec 2021 00:31:08 +0530
Subject: [PATCH 1/2] Add files via upload

---
 ExtractiveText_Summarization.ipynb | 3475 ++++++++++++++++++++++++++++
 1 file changed, 3475 insertions(+)
 create mode 100644 ExtractiveText_Summarization.ipynb

diff --git a/ExtractiveText_Summarization.ipynb b/ExtractiveText_Summarization.ipynb
new file mode 100644
index 0000000..0d99bca
--- /dev/null
+++ b/ExtractiveText_Summarization.ipynb
@@ -0,0 +1,3475 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "ExtractiveText Summarization",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "R1vqyhTZuPJ_"
+      },
+      "source": [
+        "# importing libraries"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "v12PI5x4RU4-"
+      },
+      "source": [
+        "import nltk\n",
+        "import string\n",
+        "from heapq import nlargest"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nV9ocueAvMXf"
+      },
+      "source": [
+        "# read the data"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6ooUEHapXhGG"
+      },
+      "source": [
+        "with open(\"/content/AIML.txt.txt\",\"r\") as f:\n",
+        "   text=f.read()\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 191
+        },
+        "id": "wU_q5egBa4iX",
+        "outputId": "ef140305-5f3e-4082-daae-0d2217e24723"
+      },
+      "source": [
+        "text"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "\"Big Data Is Boosting Intelligent Behavior in Machines\\nMachine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL). However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short. These fields share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines. What kind of computation and how to program it? This is not the right question. Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them. They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them.\\n\\nBig Data is no fad. The world is growing at an exponential rate and so is the size of the data collected across the globe. Data is becoming more meaningful and contextually relevant, breaking new grounds for machine learning (ML), in particular for deep learning (DL) and artificial intelligence (AI), moving them out of research labs into production (Jordan and Mitchell, 2015). The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge, conclusions, and actions. Multiple research disciplines, from cognitive sciences to biology, finance, physics, and social sciences, as well as many companies believe that data-driven and “intelligent” solutions are necessary to solve many of their key problems. High-throughput genomic and proteomic experiments can be used to enable personalized medicine. Large data sets of search queries can be used to improve information retrieval. Historical climate data can be used to understand global warming and to better predict weather. Large amounts of sensor readings and hyperspectral images of plants can be used to identify drought conditions and to gain insights into when and how stress impacts plant growth and development and in turn how to counterattack the problem of world hunger. Game data can turn pixels into actions within video games, while observational data can help enable robots to understand complex and unstructured environments and to learn manipulation skills.\\n\\nHowever, is AI, ML, and DL really synonymous, as recently suggested in the news, blogs, and media? For example, when AlphaGo (Silver et al., 2016) defeated South Korean Master Lee Se-dol in the board game Go in 2016, the terms AI, ML, and DL were used by the media to describe how AlphaGo won. In addition to this, even Gartner's list (Panetta, 2017) of top 10 Strategic Trends for 2018 places (narrow) AI at the very top, specifying it as “consisting of highly scoped machine-learning solutions that target a specific task.”\\n\\n2. Artificial Intelligence and Machine Learning\\nArtificial intelligence and ML are very much related. According to McCarthy (2007), one of the founders of the field,\\n\\nAI is “the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”\\n\\nThis is fairly generic and includes multiple tasks such as abstractly reasoning and generalizing about the world, solving puzzles, planning how to achieve goals, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (e.g., creating art or poetry), and controlling robots. Moreover, the behavior of a machine is not just the outcome of the program, it is also affected by its “body” and the enviroment it is physically embedded in. To keep it simple, however, if you can write a very clever program that has, say, human-like behavior, it can be AI. But unless it automatically learns from data, it is not ML:\\n\\nML is the science that is “concerned with the question of how to construct computer programs that automatically improve with experience,” (Mitchell, 1997).\\n\\nSo, AI and ML are both about constructing intelligent computer programs, and DL, being an instance of ML, is no exception. Deep learning (LeCun et al., 2015; Goodfellow et al., 2016), which has achieved remarkable gains in many domains spanning from object recognition, speech recognition, and control, can be viewed as constructing computer programs, namely programming layers of abstraction in a differentiable way using reusable structures such as convolution, pooling, auto encoders, variational inference networks, and so on. In other words, we replace the complexity of writing algorithms, that cover every eventuality, with the complexity of finding the right general outline of the algorithms—in the form of, for example, a deep neural network—and processing data. By virtue of the generality of neural networks—they are general function approximators—training them is data hungry and typically requires large labeled training sets. While benchmark training sets for object recognition, store hundreds or thousands of examples per class label, for many AI applications, creating labeled training data is the most time-consuming and expensive part of DL. Learning to play video games may require hundreds of hours of training experience and/or very expensive computing power. In contrast, writing an AI algorithm that covers every eventuality of a task to solve, say, reasoning about data and knowledge to label data automatically (Ratner et al., 2016; Roth, 2017) and, in turn, make, for example, DL less data-hungry–is a lot of manual work, but we know what the algorithm does by design and that it can study and that it can more easily understand the complexity of the problem it solves. When a machine has to interact with a human, this seems to be especially valuable.\\n\\nThis illustrates that ML and AI are indeed similar, but not quite the same. Artificial intelligence is about problem solving, reasoning, and learning in general. Machine learning is specifically about learning—learning from examples, from definitions, from being told, and from behavior. The easiest way to think of their relationship is to visualize them as concentric circles with AI first and ML sitting inside (with DL fitting inside both), since ML also requires writing algorithms that cover every eventuality, namely, of the learning process. The crucial point is that they share the idea of using computation as the language for intelligent behavior. What kind of computation is used and how should it be programed? This is not the right question. Computation neither rules out search, logical, probabilistic, and constraint programming techniques nor (deep) (un)supervised and reinforcement learning methods, among others, but does, as a computational model, contain all of these techniques.\\n\\nReconsidering AlphaGo: AlphaGo and its successor AlphaGo Zero (Silver et al., 2017) both combine DL and tree search—ML and AI. Alternatively, the “Allen AI Science Challenge” (Schoenick et al., 2017) should be considered. The task was to comprehend a paragraph that states a science problem, at the middle school level and then to answer a multiple-choice question. All winning models employed ML yet failed to pass the test at the level of a competent middle schooler. All winners argued that it was clear that applying a deeper, semantic level of reasoning with scientific knowledge to the question and answers, is the key to achieving true intelligence. In other words, AI has to cover knowledge, reasoning, and learning, using programmed and learning-based programmed models in a combined fashion.\\n\\n\""
+            ]
+          },
+          "execution_count": 177,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IUa0KU-7cqoI"
+      },
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "from wordcloud import WordCloud, STOPWORDS\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6ISZBaYtwU7k",
+        "outputId": "c1dda42c-4b0b-4c8e-b98b-5c5e5808d6f7"
+      },
+      "source": [
+        "STOPWORDS"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'a',\n",
+              " 'about',\n",
+              " 'above',\n",
+              " 'after',\n",
+              " 'again',\n",
+              " 'against',\n",
+              " 'all',\n",
+              " 'also',\n",
+              " 'am',\n",
+              " 'an',\n",
+              " 'and',\n",
+              " 'any',\n",
+              " 'are',\n",
+              " \"aren't\",\n",
+              " 'as',\n",
+              " 'at',\n",
+              " 'be',\n",
+              " 'because',\n",
+              " 'been',\n",
+              " 'before',\n",
+              " 'being',\n",
+              " 'below',\n",
+              " 'between',\n",
+              " 'both',\n",
+              " 'but',\n",
+              " 'by',\n",
+              " 'can',\n",
+              " \"can't\",\n",
+              " 'cannot',\n",
+              " 'com',\n",
+              " 'could',\n",
+              " \"couldn't\",\n",
+              " 'did',\n",
+              " \"didn't\",\n",
+              " 'do',\n",
+              " 'does',\n",
+              " \"doesn't\",\n",
+              " 'doing',\n",
+              " \"don't\",\n",
+              " 'down',\n",
+              " 'during',\n",
+              " 'each',\n",
+              " 'else',\n",
+              " 'ever',\n",
+              " 'few',\n",
+              " 'for',\n",
+              " 'from',\n",
+              " 'further',\n",
+              " 'get',\n",
+              " 'had',\n",
+              " \"hadn't\",\n",
+              " 'has',\n",
+              " \"hasn't\",\n",
+              " 'have',\n",
+              " \"haven't\",\n",
+              " 'having',\n",
+              " 'he',\n",
+              " \"he'd\",\n",
+              " \"he'll\",\n",
+              " \"he's\",\n",
+              " 'her',\n",
+              " 'here',\n",
+              " \"here's\",\n",
+              " 'hers',\n",
+              " 'herself',\n",
+              " 'him',\n",
+              " 'himself',\n",
+              " 'his',\n",
+              " 'how',\n",
+              " \"how's\",\n",
+              " 'however',\n",
+              " 'http',\n",
+              " 'i',\n",
+              " \"i'd\",\n",
+              " \"i'll\",\n",
+              " \"i'm\",\n",
+              " \"i've\",\n",
+              " 'if',\n",
+              " 'in',\n",
+              " 'into',\n",
+              " 'is',\n",
+              " \"isn't\",\n",
+              " 'it',\n",
+              " \"it's\",\n",
+              " 'its',\n",
+              " 'itself',\n",
+              " 'just',\n",
+              " 'k',\n",
+              " \"let's\",\n",
+              " 'like',\n",
+              " 'me',\n",
+              " 'more',\n",
+              " 'most',\n",
+              " \"mustn't\",\n",
+              " 'my',\n",
+              " 'myself',\n",
+              " 'no',\n",
+              " 'nor',\n",
+              " 'not',\n",
+              " 'of',\n",
+              " 'off',\n",
+              " 'on',\n",
+              " 'once',\n",
+              " 'only',\n",
+              " 'or',\n",
+              " 'other',\n",
+              " 'otherwise',\n",
+              " 'ought',\n",
+              " 'our',\n",
+              " 'ours',\n",
+              " 'ourselves',\n",
+              " 'out',\n",
+              " 'over',\n",
+              " 'own',\n",
+              " 'r',\n",
+              " 'same',\n",
+              " 'shall',\n",
+              " \"shan't\",\n",
+              " 'she',\n",
+              " \"she'd\",\n",
+              " \"she'll\",\n",
+              " \"she's\",\n",
+              " 'should',\n",
+              " \"shouldn't\",\n",
+              " 'since',\n",
+              " 'so',\n",
+              " 'some',\n",
+              " 'such',\n",
+              " 'than',\n",
+              " 'that',\n",
+              " \"that's\",\n",
+              " 'the',\n",
+              " 'their',\n",
+              " 'theirs',\n",
+              " 'them',\n",
+              " 'themselves',\n",
+              " 'then',\n",
+              " 'there',\n",
+              " \"there's\",\n",
+              " 'these',\n",
+              " 'they',\n",
+              " \"they'd\",\n",
+              " \"they'll\",\n",
+              " \"they're\",\n",
+              " \"they've\",\n",
+              " 'this',\n",
+              " 'those',\n",
+              " 'through',\n",
+              " 'to',\n",
+              " 'too',\n",
+              " 'under',\n",
+              " 'until',\n",
+              " 'up',\n",
+              " 'very',\n",
+              " 'was',\n",
+              " \"wasn't\",\n",
+              " 'we',\n",
+              " \"we'd\",\n",
+              " \"we'll\",\n",
+              " \"we're\",\n",
+              " \"we've\",\n",
+              " 'were',\n",
+              " \"weren't\",\n",
+              " 'what',\n",
+              " \"what's\",\n",
+              " 'when',\n",
+              " \"when's\",\n",
+              " 'where',\n",
+              " \"where's\",\n",
+              " 'which',\n",
+              " 'while',\n",
+              " 'who',\n",
+              " \"who's\",\n",
+              " 'whom',\n",
+              " 'why',\n",
+              " \"why's\",\n",
+              " 'with',\n",
+              " \"won't\",\n",
+              " 'would',\n",
+              " \"wouldn't\",\n",
+              " 'www',\n",
+              " 'you',\n",
+              " \"you'd\",\n",
+              " \"you'll\",\n",
+              " \"you're\",\n",
+              " \"you've\",\n",
+              " 'your',\n",
+              " 'yours',\n",
+              " 'yourself',\n",
+              " 'yourselves'}"
+            ]
+          },
+          "execution_count": 179,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "25PYSFt0xrjd",
+        "outputId": "3d5ed572-0b87-4294-8be5-b0570014334c"
+      },
+      "source": [
+        "print(len(text))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "7780\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "qvS0F76y4hpa",
+        "outputId": "7f063c76-32c4-42e6-be1c-11056d0901ab"
+      },
+      "source": [
+        "WC=WordCloud(stopwords=STOPWORDS, background_color=\"white\").generate(text)\n",
+        "\n",
+        "plt.figure(figsize=(15,10))\n",
+        "plt.imshow(WC,interpolation='bilinear')\n",
+        "plt.axis(\"off\")\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x720 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "IMWgTJ3M8kPu"
+      },
+      "source": [
+        "#Keyword Extraction\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KgLqtDCzAA-A",
+        "outputId": "31817ef2-3a73-4fad-f819-6630edbf73cb"
+      },
+      "source": [
+        "import nltk\n",
+        "nltk.download(\"stopwords\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kKuh19OTAda7",
+        "outputId": "48aff99f-0ef6-4a4d-96fb-a4981605afa0"
+      },
+      "source": [
+        "pip install rake_nltk"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: rake_nltk in /usr/local/lib/python3.7/dist-packages (1.0.6)\n",
+            "Requirement already satisfied: nltk<4.0.0,>=3.6.2 in /usr/local/lib/python3.7/dist-packages (from rake_nltk) (3.6.5)\n",
+            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (1.1.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (4.62.3)\n",
+            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (7.1.2)\n",
+            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk<4.0.0,>=3.6.2->rake_nltk) (2021.11.10)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mxyZ_AL6CByS",
+        "outputId": "5584e274-817c-43ea-d7b3-4d4f85432535"
+      },
+      "source": [
+        " import nltk\n",
+        " nltk.download('punkt')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Package punkt is already up-to-date!\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "execution_count": 185,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h3CBLIThAw7h",
+        "outputId": "8c755ef2-91d6-4870-c7d3-fcb1dddfac28"
+      },
+      "source": [
+        "from rake_nltk import Rake\n",
+        "rk=Rake()\n",
+        "\n",
+        "rk.extract_keywords_from_text(text)\n",
+        "extract_keyword=rk.get_ranked_phrases_with_scores()\n",
+        "extract_keyword"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[(36.0, 'defeated south korean master lee se'),\n",
+              " (32.0, 'play video games may require hundreds'),\n",
+              " (29.8, 'winning models employed ml yet failed'),\n",
+              " (29.8, '), since ml also requires writing algorithms'),\n",
+              " (29.416666666666668, 'goodfellow et al ., 2016 ),'),\n",
+              " (29.166666666666668, 'typically requires large labeled training sets'),\n",
+              " (24.583333333333336, 'silver et al ., 2016'),\n",
+              " (24.583333333333336, 'ratner et al ., 2016'),\n",
+              " (23.833333333333336, 'lecun et al ., 2015'),\n",
+              " (23.598039215686274, '“ allen ai science challenge ”'),\n",
+              " (23.333333333333336, 'silver et al ., 2017'),\n",
+              " (23.333333333333336, 'schoenick et al ., 2017'),\n",
+              " (20.75, 'differentiable way using reusable structures'),\n",
+              " (20.285714285714285, 'general function approximators — training'),\n",
+              " (17.0, 'g ., creating art'),\n",
+              " (16.5, 'actions within video games'),\n",
+              " (16.0, 'stress impacts plant growth'),\n",
+              " (15.117647058823529, 'creating labeled training data'),\n",
+              " (14.5, 'top 10 strategic trends'),\n",
+              " (14.0, 'examples per class label'),\n",
+              " (13.785714285714285, 'constructing intelligent computer programs'),\n",
+              " (13.619047619047619, '“ intelligent ” solutions'),\n",
+              " (13.5, 'specific task .” 2'),\n",
+              " (13.285714285714285, 'especially intelligent computer programs'),\n",
+              " (12.785714285714285, 'deep neural network —'),\n",
+              " (11.419047619047618, 'tree search — ml'),\n",
+              " (11.0, 'benchmark training sets'),\n",
+              " (10.5, 'constructing computer programs'),\n",
+              " (10.333333333333332, '“ body ”'),\n",
+              " (10.0, 'construct computer programs'),\n",
+              " (9.833333333333334, '2007 ), one'),\n",
+              " (9.785714285714285, 'neural networks —'),\n",
+              " (9.784313725490197, 'large data sets'),\n",
+              " (9.5, 'biologically observable .”'),\n",
+              " (9.433613445378151, 'machine learning artificial intelligence'),\n",
+              " (9.166666666666666, 'based programmed models'),\n",
+              " (9.0, 'variational inference networks'),\n",
+              " (9.0, 'next breakthrough lies'),\n",
+              " (9.0, 'learn manipulation skills'),\n",
+              " (9.0, 'identify drought conditions'),\n",
+              " (9.0, 'enable personalized medicine'),\n",
+              " (9.0, 'covers every eventuality'),\n",
+              " (9.0, 'competent middle schooler'),\n",
+              " (9.0, 'breaking new grounds'),\n",
+              " (9.0, 'better predict weather'),\n",
+              " (9.0, 'achieved remarkable gains'),\n",
+              " (8.666666666666666, 'cover every eventuality'),\n",
+              " (8.666666666666666, 'cover every eventuality'),\n",
+              " (8.619047619047619, 'making intelligent machines'),\n",
+              " (8.6, 'many domains spanning'),\n",
+              " (8.6, 'many companies believe'),\n",
+              " (8.571428571428571, 'achieving true intelligence'),\n",
+              " (8.5, 'understand global warming'),\n",
+              " (8.5, 'improve information retrieval'),\n",
+              " (8.5, 'help enable robots'),\n",
+              " (8.5, 'expensive computing power'),\n",
+              " (8.5, 'collecting massive amounts'),\n",
+              " (8.5, 'board game go'),\n",
+              " (8.333333333333334, 'right general outline'),\n",
+              " (8.333333333333334, 'includes multiple tasks'),\n",
+              " (8.285714285714285, 'highly scoped machine'),\n",
+              " (8.285714285714285, 'boosting intelligent behavior'),\n",
+              " (8.133333333333333, 'ml sitting inside'),\n",
+              " (8.117647058823529, 'historical climate data'),\n",
+              " (8.117647058823529, 'data collected across'),\n",
+              " (8.098039215686274, 'ai ), moving'),\n",
+              " (8.0, 'namely programming layers'),\n",
+              " (8.0, 'middle school level'),\n",
+              " (8.0, 'constraint programming techniques'),\n",
+              " (7.950980392156863, 'label data automatically'),\n",
+              " (7.833333333333333, 'computation neither rules'),\n",
+              " (7.833333333333333, 'computation neither rules'),\n",
+              " (7.8, 'dl really synonymous'),\n",
+              " (7.8, 'dl fitting inside'),\n",
+              " (7.785714285714286, 'model intelligent behavior'),\n",
+              " (7.6386554621848735, 'learning — learning'),\n",
+              " (7.6, 'successor alphago zero'),\n",
+              " (7.509803921568627, 'reinforcement learning methods'),\n",
+              " (7.509803921568627, 'reinforcement learning methods'),\n",
+              " (7.364705882352942, 'many ai applications'),\n",
+              " (7.333333333333334, 'multiple research disciplines'),\n",
+              " (7.0, 'writing algorithms'),\n",
+              " (6.9523809523809526, 'algorithms —'),\n",
+              " (6.91764705882353, 'dl less data'),\n",
+              " (6.795518207282912, 'machines machine learning'),\n",
+              " (6.738095238095239, 'understand human intelligence'),\n",
+              " (6.666666666666667, 'becoming dominant problem'),\n",
+              " (6.166666666666666, 'large amounts'),\n",
+              " (6.0, 'training experience'),\n",
+              " (6.0, 'store hundreds'),\n",
+              " (5.966666666666667, 'ml ),'),\n",
+              " (5.833333333333334, 'poetry ),'),\n",
+              " (5.666666666666666, 'computational models'),\n",
+              " (5.5, '2015 ).'),\n",
+              " (5.333333333333334, 'also affected'),\n",
+              " (5.285714285714286, '— turning'),\n",
+              " (5.285714285714286, 'intelligent behavior'),\n",
+              " (5.25, 'using programmed'),\n",
+              " (5.1764705882352935, 'learning solutions'),\n",
+              " (5.0, '“ consisting'),\n",
+              " (5.0, '“ concerned'),\n",
+              " (5.0, 'useful way'),\n",
+              " (5.0, 'especially valuable'),\n",
+              " (5.0, 'easiest way'),\n",
+              " (4.9714285714285715, 'artificial intelligence'),\n",
+              " (4.9714285714285715, 'artificial intelligence'),\n",
+              " (4.9714285714285715, 'artificial intelligence'),\n",
+              " (4.9714285714285715, 'artificial intelligence'),\n",
+              " (4.833333333333334, 'automatically improve'),\n",
+              " (4.75, 'using computers'),\n",
+              " (4.617647058823529, 'game data'),\n",
+              " (4.6, 'many areas'),\n",
+              " (4.583333333333333, 'using computation'),\n",
+              " (4.5, 'understand complex'),\n",
+              " (4.5, 'moving around'),\n",
+              " (4.5, 'expensive part'),\n",
+              " (4.5, 'easily understand'),\n",
+              " (4.5, 'controlling robots'),\n",
+              " (4.5, 'computational model'),\n",
+              " (4.46218487394958, 'machine learning'),\n",
+              " (4.46218487394958, 'machine learning'),\n",
+              " (4.333333333333334, 'automatically learns'),\n",
+              " (4.25, '2016'),\n",
+              " (4.1764705882352935, 'learning process'),\n",
+              " (4.1764705882352935, 'deep learning'),\n",
+              " (4.1764705882352935, 'deep learning'),\n",
+              " (4.1764705882352935, 'deep learning'),\n",
+              " (4.166666666666667, 'science problem'),\n",
+              " (4.166666666666666, 'cover knowledge'),\n",
+              " (4.117647058823529, 'processing data'),\n",
+              " (4.117647058823529, 'observational data'),\n",
+              " (4.117647058823529, 'data hungry'),\n",
+              " (4.117647058823529, 'big data'),\n",
+              " (4.117647058823529, 'big data'),\n",
+              " (4.1, 'solve many'),\n",
+              " (4.0, 'winners argued'),\n",
+              " (4.0, 'unstructured environments'),\n",
+              " (4.0, 'throughput genomic'),\n",
+              " (4.0, 'speech recognition'),\n",
+              " (4.0, 'solving techniques'),\n",
+              " (4.0, 'solving puzzles'),\n",
+              " (4.0, 'social sciences'),\n",
+              " (4.0, 'similar task'),\n",
+              " (4.0, 'sensor readings'),\n",
+              " (4.0, 'semantic level'),\n",
+              " (4.0, 'search queries'),\n",
+              " (4.0, 'research labs'),\n",
+              " (4.0, 'recognizing objects'),\n",
+              " (4.0, 'recently suggested'),\n",
+              " (4.0, 'recently suggested'),\n",
+              " (4.0, 'recent successes'),\n",
+              " (4.0, 'proteomic experiments'),\n",
+              " (4.0, 'physically embedded'),\n",
+              " (4.0, 'performing social'),\n",
+              " (4.0, 'object recognition'),\n",
+              " (4.0, 'object recognition'),\n",
+              " (4.0, 'manual work'),\n",
+              " (4.0, 'like behavior'),\n",
+              " (4.0, 'indeed similar'),\n",
+              " (4.0, 'hyperspectral images'),\n",
+              " (4.0, 'hungry –'),\n",
+              " (4.0, 'gain insights'),\n",
+              " (4.0, 'fundamental hypotheses'),\n",
+              " (4.0, 'fairly generic'),\n",
+              " (4.0, 'exponential rate'),\n",
+              " (4.0, 'experience ,”'),\n",
+              " (4.0, 'even gartner'),\n",
+              " (4.0, 'crucial point'),\n",
+              " (4.0, 'creative work'),\n",
+              " (4.0, 'contextually relevant'),\n",
+              " (4.0, 'concentric circles'),\n",
+              " (4.0, 'combined fashion'),\n",
+              " (4.0, 'cognitive sciences'),\n",
+              " (4.0, 'business transactions'),\n",
+              " (4.0, 'auto encoders'),\n",
+              " (4.0, 'among others'),\n",
+              " (4.0, 'among others'),\n",
+              " (4.0, 'achieve goals'),\n",
+              " (4.0, '2018 places'),\n",
+              " (4.0, '1997 ).'),\n",
+              " (3.9333333333333336, 'right question'),\n",
+              " (3.9333333333333336, 'right question'),\n",
+              " (3.8, 'dl ).'),\n",
+              " (3.8, 'combine dl'),\n",
+              " (3.764705882352941, 'terms ai'),\n",
+              " (3.764705882352941, 'equation ai'),\n",
+              " (3.764705882352941, 'ai first'),\n",
+              " (3.666666666666667, 'problem solving'),\n",
+              " (3.6, 'reconsidering alphago'),\n",
+              " (3.6, 'choice question'),\n",
+              " (3.5, 'scientific knowledge'),\n",
+              " (3.5, 'probabilistic techniques'),\n",
+              " (3.5, 'much related'),\n",
+              " (3.5, 'key problems'),\n",
+              " (3.5, 'fields share'),\n",
+              " (3.3333333333333335, 'writing'),\n",
+              " (3.3333333333333335, 'also'),\n",
+              " (3.333333333333333, 'turn pixels'),\n",
+              " (3.333333333333333, 'clever program'),\n",
+              " (3.264705882352941, 'ai algorithm'),\n",
+              " (3.25, 'world hunger'),\n",
+              " (3.2, 'abstractly reasoning'),\n",
+              " (3.0, '“'),\n",
+              " (3.0, 'general'),\n",
+              " (3.0, '2017'),\n",
+              " (3.0, '2017'),\n",
+              " (2.5, 'top'),\n",
+              " (2.5, 'science'),\n",
+              " (2.5, 'science'),\n",
+              " (2.5, 'examples'),\n",
+              " (2.5, 'actions'),\n",
+              " (2.3333333333333335, 'multiple'),\n",
+              " (2.3333333333333335, 'methods'),\n",
+              " (2.3333333333333335, 'machines'),\n",
+              " (2.2857142857142856, 'machine'),\n",
+              " (2.2857142857142856, 'machine'),\n",
+              " (2.176470588235294, 'learning'),\n",
+              " (2.176470588235294, 'learning'),\n",
+              " (2.176470588235294, 'learning'),\n",
+              " (2.176470588235294, 'learning'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1333333333333333, 'ml'),\n",
+              " (2.1176470588235294, 'data'),\n",
+              " (2.1176470588235294, 'data'),\n",
+              " (2.1176470588235294, 'data'),\n",
+              " (2.1176470588235294, 'data'),\n",
+              " (2.1176470588235294, 'data'),\n",
+              " (2.0, 'techniques'),\n",
+              " (2.0, 'task'),\n",
+              " (2.0, 'task'),\n",
+              " (2.0, 'search'),\n",
+              " (2.0, 'search'),\n",
+              " (2.0, 'research'),\n",
+              " (2.0, 'namely'),\n",
+              " (2.0, 'level'),\n",
+              " (2.0, 'deep'),\n",
+              " (2.0, 'deep'),\n",
+              " (2.0, 'behavior'),\n",
+              " (2.0, 'behavior'),\n",
+              " (2.0, 'becoming'),\n",
+              " (1.8333333333333333, 'computation'),\n",
+              " (1.8333333333333333, 'computation'),\n",
+              " (1.8333333333333333, 'computation'),\n",
+              " (1.8, 'dl'),\n",
+              " (1.8, 'dl'),\n",
+              " (1.8, 'dl'),\n",
+              " (1.8, 'dl'),\n",
+              " (1.8, 'dl'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.7647058823529411, 'ai'),\n",
+              " (1.6666666666666667, 'problem'),\n",
+              " (1.6666666666666667, 'problem'),\n",
+              " (1.6666666666666667, 'problem'),\n",
+              " (1.6666666666666667, 'human'),\n",
+              " (1.6666666666666667, 'human'),\n",
+              " (1.6, 'question'),\n",
+              " (1.6, 'question'),\n",
+              " (1.6, 'alphago'),\n",
+              " (1.6, 'alphago'),\n",
+              " (1.6, 'alphago'),\n",
+              " (1.5, 'solve'),\n",
+              " (1.5, 'share'),\n",
+              " (1.5, 'related'),\n",
+              " (1.5, 'probabilistic'),\n",
+              " (1.5, 'knowledge'),\n",
+              " (1.5, 'knowledge'),\n",
+              " (1.5, 'key'),\n",
+              " (1.5, 'algorithm'),\n",
+              " (1.3333333333333333, 'turn'),\n",
+              " (1.3333333333333333, 'turn'),\n",
+              " (1.3333333333333333, 'program'),\n",
+              " (1.3333333333333333, 'program'),\n",
+              " (1.25, 'world'),\n",
+              " (1.25, 'world'),\n",
+              " (1.25, 'world'),\n",
+              " (1.2, 'reasoning'),\n",
+              " (1.2, 'reasoning'),\n",
+              " (1.2, 'reasoning'),\n",
+              " (1.2, 'reasoning'),\n",
+              " (1.0, 'write'),\n",
+              " (1.0, 'words'),\n",
+              " (1.0, 'words'),\n",
+              " (1.0, 'well'),\n",
+              " (1.0, 'visualize'),\n",
+              " (1.0, 'virtue'),\n",
+              " (1.0, 'viewed'),\n",
+              " (1.0, 'used'),\n",
+              " (1.0, 'used'),\n",
+              " (1.0, 'used'),\n",
+              " (1.0, 'used'),\n",
+              " (1.0, 'used'),\n",
+              " (1.0, 'used'),\n",
+              " (1.0, 'unless'),\n",
+              " (1.0, 'understanding'),\n",
+              " (1.0, 'un'),\n",
+              " (1.0, 'un'),\n",
+              " (1.0, 'translating'),\n",
+              " (1.0, 'told'),\n",
+              " (1.0, 'time'),\n",
+              " (1.0, 'thousands'),\n",
+              " (1.0, 'think'),\n",
+              " (1.0, 'test'),\n",
+              " (1.0, 'target'),\n",
+              " (1.0, 'supervised'),\n",
+              " (1.0, 'supervised'),\n",
+              " (1.0, 'study'),\n",
+              " (1.0, 'states'),\n",
+              " (1.0, 'specifying'),\n",
+              " (1.0, 'specifically'),\n",
+              " (1.0, 'speaking'),\n",
+              " (1.0, 'sounds'),\n",
+              " (1.0, 'solves'),\n",
+              " (1.0, 'size'),\n",
+              " (1.0, 'simple'),\n",
+              " (1.0, 'short'),\n",
+              " (1.0, 'shifted'),\n",
+              " (1.0, 'seems'),\n",
+              " (1.0, 'say'),\n",
+              " (1.0, 'say'),\n",
+              " (1.0, 'roth'),\n",
+              " (1.0, 'replace'),\n",
+              " (1.0, 'relationship'),\n",
+              " (1.0, 'quite'),\n",
+              " (1.0, 'pushing'),\n",
+              " (1.0, 'programed'),\n",
+              " (1.0, 'production'),\n",
+              " (1.0, 'pooling'),\n",
+              " (1.0, 'plants'),\n",
+              " (1.0, 'planning'),\n",
+              " (1.0, 'physics'),\n",
+              " (1.0, 'pass'),\n",
+              " (1.0, 'particular'),\n",
+              " (1.0, 'paragraph'),\n",
+              " (1.0, 'panetta'),\n",
+              " (1.0, 'outcome'),\n",
+              " (1.0, 'news'),\n",
+              " (1.0, 'news'),\n",
+              " (1.0, 'necessary'),\n",
+              " (1.0, 'narrow'),\n",
+              " (1.0, 'moreover'),\n",
+              " (1.0, 'mitchell'),\n",
+              " (1.0, 'mitchell'),\n",
+              " (1.0, 'media'),\n",
+              " (1.0, 'media'),\n",
+              " (1.0, 'media'),\n",
+              " (1.0, 'meaningful'),\n",
+              " (1.0, 'mccarthy'),\n",
+              " (1.0, 'make'),\n",
+              " (1.0, 'lot'),\n",
+              " (1.0, 'logical'),\n",
+              " (1.0, 'logical'),\n",
+              " (1.0, 'list'),\n",
+              " (1.0, 'least'),\n",
+              " (1.0, 'language'),\n",
+              " (1.0, 'know'),\n",
+              " (1.0, 'kind'),\n",
+              " (1.0, 'kind'),\n",
+              " (1.0, 'keep'),\n",
+              " (1.0, 'jordan'),\n",
+              " (1.0, 'interact'),\n",
+              " (1.0, 'instance'),\n",
+              " (1.0, 'industry'),\n",
+              " (1.0, 'include'),\n",
+              " (1.0, 'illustrates'),\n",
+              " (1.0, 'idea'),\n",
+              " (1.0, 'however'),\n",
+              " (1.0, 'however'),\n",
+              " (1.0, 'however'),\n",
+              " (1.0, 'hours'),\n",
+              " (1.0, 'high'),\n",
+              " (1.0, 'growing'),\n",
+              " (1.0, 'globe'),\n",
+              " (1.0, 'generalizing'),\n",
+              " (1.0, 'generality'),\n",
+              " (1.0, 'founders'),\n",
+              " (1.0, 'form'),\n",
+              " (1.0, 'finding'),\n",
+              " (1.0, 'finance'),\n",
+              " (1.0, 'field'),\n",
+              " (1.0, 'falls'),\n",
+              " (1.0, 'fad'),\n",
+              " (1.0, 'exception'),\n",
+              " (1.0, 'example'),\n",
+              " (1.0, 'example'),\n",
+              " (1.0, 'example'),\n",
+              " (1.0, 'enviroment'),\n",
+              " (1.0, 'engineering'),\n",
+              " (1.0, 'e'),\n",
+              " (1.0, 'driven'),\n",
+              " (1.0, 'dol'),\n",
+              " (1.0, 'development'),\n",
+              " (1.0, 'design'),\n",
+              " (1.0, 'describe'),\n",
+              " (1.0, 'definitions'),\n",
+              " (1.0, 'deeper'),\n",
+              " (1.0, 'counterattack'),\n",
+              " (1.0, 'convolution'),\n",
+              " (1.0, 'control'),\n",
+              " (1.0, 'contrast'),\n",
+              " (1.0, 'contain'),\n",
+              " (1.0, 'consuming'),\n",
+              " (1.0, 'considered'),\n",
+              " (1.0, 'confine'),\n",
+              " (1.0, 'conclusions'),\n",
+              " (1.0, 'comprehend'),\n",
+              " (1.0, 'complexity'),\n",
+              " (1.0, 'complexity'),\n",
+              " (1.0, 'complexity'),\n",
+              " (1.0, 'complement'),\n",
+              " (1.0, 'combining'),\n",
+              " (1.0, 'clear'),\n",
+              " (1.0, 'blogs'),\n",
+              " (1.0, 'blogs'),\n",
+              " (1.0, 'biology'),\n",
+              " (1.0, 'applying'),\n",
+              " (1.0, 'answers'),\n",
+              " (1.0, 'answer'),\n",
+              " (1.0, 'alternatively'),\n",
+              " (1.0, 'addition'),\n",
+              " (1.0, 'according'),\n",
+              " (1.0, 'abstraction')]"
+            ]
+          },
+          "execution_count": 186,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ngE15atlOWrV",
+        "outputId": "3e4f21d4-a897-43fb-a687-bfafcdb1c61d"
+      },
+      "source": [
+        "from rake_nltk import Rake\n",
+        "rk=Rake()\n",
+        "\n",
+        "rk.extract_keywords_from_text(text)\n",
+        "extract_keyword=rk.get_word_frequency_distribution()\n",
+        "extract_keyword"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Counter({'),': 6,\n",
+              "         ').': 3,\n",
+              "         ',”': 1,\n",
+              "         '.,': 7,\n",
+              "         '.”': 2,\n",
+              "         '10': 1,\n",
+              "         '1997': 1,\n",
+              "         '2': 1,\n",
+              "         '2007': 1,\n",
+              "         '2015': 2,\n",
+              "         '2016': 4,\n",
+              "         '2017': 4,\n",
+              "         '2018': 1,\n",
+              "         'abstraction': 1,\n",
+              "         'abstractly': 1,\n",
+              "         'according': 1,\n",
+              "         'achieve': 1,\n",
+              "         'achieved': 1,\n",
+              "         'achieving': 1,\n",
+              "         'across': 1,\n",
+              "         'actions': 2,\n",
+              "         'addition': 1,\n",
+              "         'affected': 1,\n",
+              "         'ai': 17,\n",
+              "         'al': 6,\n",
+              "         'algorithm': 2,\n",
+              "         'algorithms': 3,\n",
+              "         'allen': 1,\n",
+              "         'alphago': 5,\n",
+              "         'also': 3,\n",
+              "         'alternatively': 1,\n",
+              "         'among': 2,\n",
+              "         'amounts': 2,\n",
+              "         'answer': 1,\n",
+              "         'answers': 1,\n",
+              "         'applications': 1,\n",
+              "         'applying': 1,\n",
+              "         'approximators': 1,\n",
+              "         'areas': 1,\n",
+              "         'argued': 1,\n",
+              "         'around': 1,\n",
+              "         'art': 1,\n",
+              "         'artificial': 5,\n",
+              "         'auto': 1,\n",
+              "         'automatically': 3,\n",
+              "         'based': 1,\n",
+              "         'becoming': 2,\n",
+              "         'behavior': 6,\n",
+              "         'believe': 1,\n",
+              "         'benchmark': 1,\n",
+              "         'better': 1,\n",
+              "         'big': 2,\n",
+              "         'biologically': 1,\n",
+              "         'biology': 1,\n",
+              "         'blogs': 2,\n",
+              "         'board': 1,\n",
+              "         'body': 1,\n",
+              "         'boosting': 1,\n",
+              "         'breaking': 1,\n",
+              "         'breakthrough': 1,\n",
+              "         'business': 1,\n",
+              "         'challenge': 1,\n",
+              "         'choice': 1,\n",
+              "         'circles': 1,\n",
+              "         'class': 1,\n",
+              "         'clear': 1,\n",
+              "         'clever': 1,\n",
+              "         'climate': 1,\n",
+              "         'cognitive': 1,\n",
+              "         'collected': 1,\n",
+              "         'collecting': 1,\n",
+              "         'combine': 1,\n",
+              "         'combined': 1,\n",
+              "         'combining': 1,\n",
+              "         'companies': 1,\n",
+              "         'competent': 1,\n",
+              "         'complement': 1,\n",
+              "         'complex': 1,\n",
+              "         'complexity': 3,\n",
+              "         'comprehend': 1,\n",
+              "         'computation': 6,\n",
+              "         'computational': 2,\n",
+              "         'computer': 4,\n",
+              "         'computers': 1,\n",
+              "         'computing': 1,\n",
+              "         'concentric': 1,\n",
+              "         'concerned': 1,\n",
+              "         'conclusions': 1,\n",
+              "         'conditions': 1,\n",
+              "         'confine': 1,\n",
+              "         'considered': 1,\n",
+              "         'consisting': 1,\n",
+              "         'constraint': 1,\n",
+              "         'construct': 1,\n",
+              "         'constructing': 2,\n",
+              "         'consuming': 1,\n",
+              "         'contain': 1,\n",
+              "         'contextually': 1,\n",
+              "         'contrast': 1,\n",
+              "         'control': 1,\n",
+              "         'controlling': 1,\n",
+              "         'convolution': 1,\n",
+              "         'counterattack': 1,\n",
+              "         'cover': 3,\n",
+              "         'covers': 1,\n",
+              "         'creating': 2,\n",
+              "         'creative': 1,\n",
+              "         'crucial': 1,\n",
+              "         'data': 17,\n",
+              "         'deep': 6,\n",
+              "         'deeper': 1,\n",
+              "         'defeated': 1,\n",
+              "         'definitions': 1,\n",
+              "         'describe': 1,\n",
+              "         'design': 1,\n",
+              "         'development': 1,\n",
+              "         'differentiable': 1,\n",
+              "         'disciplines': 1,\n",
+              "         'dl': 10,\n",
+              "         'dol': 1,\n",
+              "         'domains': 1,\n",
+              "         'dominant': 1,\n",
+              "         'driven': 1,\n",
+              "         'drought': 1,\n",
+              "         'e': 1,\n",
+              "         'easiest': 1,\n",
+              "         'easily': 1,\n",
+              "         'embedded': 1,\n",
+              "         'employed': 1,\n",
+              "         'enable': 2,\n",
+              "         'encoders': 1,\n",
+              "         'engineering': 1,\n",
+              "         'enviroment': 1,\n",
+              "         'environments': 1,\n",
+              "         'equation': 1,\n",
+              "         'especially': 2,\n",
+              "         'et': 6,\n",
+              "         'even': 1,\n",
+              "         'eventuality': 3,\n",
+              "         'every': 3,\n",
+              "         'example': 3,\n",
+              "         'examples': 2,\n",
+              "         'exception': 1,\n",
+              "         'expensive': 2,\n",
+              "         'experience': 2,\n",
+              "         'experiments': 1,\n",
+              "         'exponential': 1,\n",
+              "         'fad': 1,\n",
+              "         'failed': 1,\n",
+              "         'fairly': 1,\n",
+              "         'falls': 1,\n",
+              "         'fashion': 1,\n",
+              "         'field': 1,\n",
+              "         'fields': 1,\n",
+              "         'finance': 1,\n",
+              "         'finding': 1,\n",
+              "         'first': 1,\n",
+              "         'fitting': 1,\n",
+              "         'form': 1,\n",
+              "         'founders': 1,\n",
+              "         'function': 1,\n",
+              "         'fundamental': 1,\n",
+              "         'g': 1,\n",
+              "         'gain': 1,\n",
+              "         'gains': 1,\n",
+              "         'game': 2,\n",
+              "         'games': 2,\n",
+              "         'gartner': 1,\n",
+              "         'general': 3,\n",
+              "         'generality': 1,\n",
+              "         'generalizing': 1,\n",
+              "         'generic': 1,\n",
+              "         'genomic': 1,\n",
+              "         'global': 1,\n",
+              "         'globe': 1,\n",
+              "         'go': 1,\n",
+              "         'goals': 1,\n",
+              "         'goodfellow': 1,\n",
+              "         'grounds': 1,\n",
+              "         'growing': 1,\n",
+              "         'growth': 1,\n",
+              "         'help': 1,\n",
+              "         'high': 1,\n",
+              "         'highly': 1,\n",
+              "         'historical': 1,\n",
+              "         'hours': 1,\n",
+              "         'however': 3,\n",
+              "         'human': 3,\n",
+              "         'hundreds': 2,\n",
+              "         'hunger': 1,\n",
+              "         'hungry': 2,\n",
+              "         'hyperspectral': 1,\n",
+              "         'hypotheses': 1,\n",
+              "         'idea': 1,\n",
+              "         'identify': 1,\n",
+              "         'illustrates': 1,\n",
+              "         'images': 1,\n",
+              "         'impacts': 1,\n",
+              "         'improve': 2,\n",
+              "         'include': 1,\n",
+              "         'includes': 1,\n",
+              "         'indeed': 1,\n",
+              "         'industry': 1,\n",
+              "         'inference': 1,\n",
+              "         'information': 1,\n",
+              "         'inside': 2,\n",
+              "         'insights': 1,\n",
+              "         'instance': 1,\n",
+              "         'intelligence': 7,\n",
+              "         'intelligent': 7,\n",
+              "         'interact': 1,\n",
+              "         'jordan': 1,\n",
+              "         'keep': 1,\n",
+              "         'key': 2,\n",
+              "         'kind': 2,\n",
+              "         'know': 1,\n",
+              "         'knowledge': 4,\n",
+              "         'korean': 1,\n",
+              "         'label': 2,\n",
+              "         'labeled': 2,\n",
+              "         'labs': 1,\n",
+              "         'language': 1,\n",
+              "         'large': 3,\n",
+              "         'layers': 1,\n",
+              "         'learn': 1,\n",
+              "         'learning': 17,\n",
+              "         'learns': 1,\n",
+              "         'least': 1,\n",
+              "         'lecun': 1,\n",
+              "         'lee': 1,\n",
+              "         'less': 1,\n",
+              "         'level': 3,\n",
+              "         'lies': 1,\n",
+              "         'like': 1,\n",
+              "         'list': 1,\n",
+              "         'logical': 2,\n",
+              "         'lot': 1,\n",
+              "         'machine': 7,\n",
+              "         'machines': 3,\n",
+              "         'make': 1,\n",
+              "         'making': 1,\n",
+              "         'manipulation': 1,\n",
+              "         'manual': 1,\n",
+              "         'many': 5,\n",
+              "         'massive': 1,\n",
+              "         'master': 1,\n",
+              "         'may': 1,\n",
+              "         'mccarthy': 1,\n",
+              "         'meaningful': 1,\n",
+              "         'media': 3,\n",
+              "         'medicine': 1,\n",
+              "         'methods': 3,\n",
+              "         'middle': 2,\n",
+              "         'mitchell': 2,\n",
+              "         'ml': 15,\n",
+              "         'model': 2,\n",
+              "         'models': 3,\n",
+              "         'moreover': 1,\n",
+              "         'moving': 2,\n",
+              "         'much': 1,\n",
+              "         'multiple': 3,\n",
+              "         'namely': 2,\n",
+              "         'narrow': 1,\n",
+              "         'necessary': 1,\n",
+              "         'neither': 2,\n",
+              "         'network': 1,\n",
+              "         'networks': 2,\n",
+              "         'neural': 2,\n",
+              "         'new': 1,\n",
+              "         'news': 2,\n",
+              "         'next': 1,\n",
+              "         'object': 2,\n",
+              "         'objects': 1,\n",
+              "         'observable': 1,\n",
+              "         'observational': 1,\n",
+              "         'one': 1,\n",
+              "         'others': 2,\n",
+              "         'outcome': 1,\n",
+              "         'outline': 1,\n",
+              "         'panetta': 1,\n",
+              "         'paragraph': 1,\n",
+              "         'part': 1,\n",
+              "         'particular': 1,\n",
+              "         'pass': 1,\n",
+              "         'per': 1,\n",
+              "         'performing': 1,\n",
+              "         'personalized': 1,\n",
+              "         'physically': 1,\n",
+              "         'physics': 1,\n",
+              "         'pixels': 1,\n",
+              "         'places': 1,\n",
+              "         'planning': 1,\n",
+              "         'plant': 1,\n",
+              "         'plants': 1,\n",
+              "         'play': 1,\n",
+              "         'poetry': 1,\n",
+              "         'point': 1,\n",
+              "         'pooling': 1,\n",
+              "         'power': 1,\n",
+              "         'predict': 1,\n",
+              "         'probabilistic': 2,\n",
+              "         'problem': 6,\n",
+              "         'problems': 1,\n",
+              "         'process': 1,\n",
+              "         'processing': 1,\n",
+              "         'production': 1,\n",
+              "         'program': 3,\n",
+              "         'programed': 1,\n",
+              "         'programmed': 2,\n",
+              "         'programming': 2,\n",
+              "         'programs': 4,\n",
+              "         'proteomic': 1,\n",
+              "         'pushing': 1,\n",
+              "         'puzzles': 1,\n",
+              "         'queries': 1,\n",
+              "         'question': 5,\n",
+              "         'quite': 1,\n",
+              "         'rate': 1,\n",
+              "         'ratner': 1,\n",
+              "         'readings': 1,\n",
+              "         'really': 1,\n",
+              "         'reasoning': 5,\n",
+              "         'recent': 1,\n",
+              "         'recently': 2,\n",
+              "         'recognition': 3,\n",
+              "         'recognizing': 1,\n",
+              "         'reconsidering': 1,\n",
+              "         'reinforcement': 2,\n",
+              "         'related': 2,\n",
+              "         'relationship': 1,\n",
+              "         'relevant': 1,\n",
+              "         'remarkable': 1,\n",
+              "         'replace': 1,\n",
+              "         'require': 1,\n",
+              "         'requires': 2,\n",
+              "         'research': 3,\n",
+              "         'retrieval': 1,\n",
+              "         'reusable': 1,\n",
+              "         'right': 3,\n",
+              "         'robots': 2,\n",
+              "         'roth': 1,\n",
+              "         'rules': 2,\n",
+              "         'say': 2,\n",
+              "         'schoenick': 1,\n",
+              "         'school': 1,\n",
+              "         'schooler': 1,\n",
+              "         'science': 4,\n",
+              "         'sciences': 2,\n",
+              "         'scientific': 1,\n",
+              "         'scoped': 1,\n",
+              "         'se': 1,\n",
+              "         'search': 4,\n",
+              "         'seems': 1,\n",
+              "         'semantic': 1,\n",
+              "         'sensor': 1,\n",
+              "         'sets': 3,\n",
+              "         'share': 2,\n",
+              "         'shifted': 1,\n",
+              "         'short': 1,\n",
+              "         'silver': 2,\n",
+              "         'similar': 2,\n",
+              "         'simple': 1,\n",
+              "         'since': 1,\n",
+              "         'sitting': 1,\n",
+              "         'size': 1,\n",
+              "         'skills': 1,\n",
+              "         'social': 2,\n",
+              "         'solutions': 2,\n",
+              "         'solve': 2,\n",
+              "         'solves': 1,\n",
+              "         'solving': 3,\n",
+              "         'sounds': 1,\n",
+              "         'south': 1,\n",
+              "         'spanning': 1,\n",
+              "         'speaking': 1,\n",
+              "         'specific': 1,\n",
+              "         'specifically': 1,\n",
+              "         'specifying': 1,\n",
+              "         'speech': 1,\n",
+              "         'states': 1,\n",
+              "         'store': 1,\n",
+              "         'strategic': 1,\n",
+              "         'stress': 1,\n",
+              "         'structures': 1,\n",
+              "         'study': 1,\n",
+              "         'successes': 1,\n",
+              "         'successor': 1,\n",
+              "         'suggested': 2,\n",
+              "         'supervised': 2,\n",
+              "         'synonymous': 1,\n",
+              "         'target': 1,\n",
+              "         'task': 4,\n",
+              "         'tasks': 1,\n",
+              "         'techniques': 4,\n",
+              "         'terms': 1,\n",
+              "         'test': 1,\n",
+              "         'think': 1,\n",
+              "         'thousands': 1,\n",
+              "         'throughput': 1,\n",
+              "         'time': 1,\n",
+              "         'told': 1,\n",
+              "         'top': 2,\n",
+              "         'training': 5,\n",
+              "         'transactions': 1,\n",
+              "         'translating': 1,\n",
+              "         'tree': 1,\n",
+              "         'trends': 1,\n",
+              "         'true': 1,\n",
+              "         'turn': 3,\n",
+              "         'turning': 1,\n",
+              "         'typically': 1,\n",
+              "         'un': 2,\n",
+              "         'understand': 4,\n",
+              "         'understanding': 1,\n",
+              "         'unless': 1,\n",
+              "         'unstructured': 1,\n",
+              "         'used': 6,\n",
+              "         'useful': 1,\n",
+              "         'using': 4,\n",
+              "         'valuable': 1,\n",
+              "         'variational': 1,\n",
+              "         'video': 2,\n",
+              "         'viewed': 1,\n",
+              "         'virtue': 1,\n",
+              "         'visualize': 1,\n",
+              "         'warming': 1,\n",
+              "         'way': 3,\n",
+              "         'weather': 1,\n",
+              "         'well': 1,\n",
+              "         'winners': 1,\n",
+              "         'winning': 1,\n",
+              "         'within': 1,\n",
+              "         'words': 2,\n",
+              "         'work': 2,\n",
+              "         'world': 4,\n",
+              "         'write': 1,\n",
+              "         'writing': 3,\n",
+              "         'yet': 1,\n",
+              "         'zero': 1,\n",
+              "         '–': 1,\n",
+              "         '—': 7,\n",
+              "         '“': 6,\n",
+              "         '”': 3})"
+            ]
+          },
+          "execution_count": 187,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bMOjRahLQ5fK"
+      },
+      "source": [
+        "#Text Summarization"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eeVQiY49RyyG",
+        "outputId": "182dd141-d62d-4689-b044-17e5909e348f"
+      },
+      "source": [
+        "print(text.count(\".\"))\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "55\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1msQeoroDavH"
+      },
+      "source": [
+        "# Remove punctuation"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZKkduejpVNn3",
+        "outputId": "9e9b21e2-3038-4810-e1d7-a4cc9696bdb0"
+      },
+      "source": [
+        "string.punctuation = string.punctuation + '\\n' \n",
+        "print(string.punctuation)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 191
+        },
+        "id": "TkXkMFPyB2fL",
+        "outputId": "9cbe0fb3-8035-4963-ab95-d64a4e0f8fab"
+      },
+      "source": [
+        "nopunct=[char for char in text if char not in string.punctuation]\n",
+        "nopunct=\"\".join(nopunct)\n",
+        "nopunct"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'Big Data Is Boosting Intelligent Behavior in MachinesMachine learning ML and artificial intelligence AI are becoming dominant problemsolving techniques in many areas of research and industry not least because of the recent successes of deep learning DL However the equation AIMLDL as recently suggested in the news blogs and media falls too short These fields share the same fundamental hypotheses computation is a useful way to model intelligent behavior in machines What kind of computation and how to program it This is not the right question Computation neither rules out search logical and probabilistic techniques nor deep unsupervised and reinforcement learning methods among others as computational models do include all of them They complement each other and the next breakthrough lies not only in pushing each of them but also in combining themBig Data is no fad The world is growing at an exponential rate and so is the size of the data collected across the globe Data is becoming more meaningful and contextually relevant breaking new grounds for machine learning ML in particular for deep learning DL and artificial intelligence AI moving them out of research labs into production Jordan and Mitchell 2015 The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge conclusions and actions Multiple research disciplines from cognitive sciences to biology finance physics and social sciences as well as many companies believe that datadriven and “intelligent” solutions are necessary to solve many of their key problems Highthroughput genomic and proteomic experiments can be used to enable personalized medicine Large data sets of search queries can be used to improve information retrieval Historical climate data can be used to understand global warming and to better predict weather Large amounts of sensor readings and hyperspectral images of plants can be used to identify drought conditions and to gain insights into when and how stress impacts plant growth and development and in turn how to counterattack the problem of world hunger Game data can turn pixels into actions within video games while observational data can help enable robots to understand complex and unstructured environments and to learn manipulation skillsHowever is AI ML and DL really synonymous as recently suggested in the news blogs and media For example when AlphaGo Silver et al 2016 defeated South Korean Master Lee Sedol in the board game Go in 2016 the terms AI ML and DL were used by the media to describe how AlphaGo won In addition to this even Gartners list Panetta 2017 of top 10 Strategic Trends for 2018 places narrow AI at the very top specifying it as “consisting of highly scoped machinelearning solutions that target a specific task”2 Artificial Intelligence and Machine LearningArtificial intelligence and ML are very much related According to McCarthy 2007 one of the founders of the fieldAI is “the science and engineering of making intelligent machines especially intelligent computer programs It is related to the similar task of using computers to understand human intelligence but AI does not have to confine itself to methods that are biologically observable”This is fairly generic and includes multiple tasks such as abstractly reasoning and generalizing about the world solving puzzles planning how to achieve goals moving around in the world recognizing objects and sounds speaking translating performing social or business transactions creative work eg creating art or poetry and controlling robots Moreover the behavior of a machine is not just the outcome of the program it is also affected by its “body” and the enviroment it is physically embedded in To keep it simple however if you can write a very clever program that has say humanlike behavior it can be AI But unless it automatically learns from data it is not MLML is the science that is “concerned with the question of how to construct computer programs that automatically improve with experience” Mitchell 1997So AI and ML are both about constructing intelligent computer programs and DL being an instance of ML is no exception Deep learning LeCun et al 2015 Goodfellow et al 2016 which has achieved remarkable gains in many domains spanning from object recognition speech recognition and control can be viewed as constructing computer programs namely programming layers of abstraction in a differentiable way using reusable structures such as convolution pooling auto encoders variational inference networks and so on In other words we replace the complexity of writing algorithms that cover every eventuality with the complexity of finding the right general outline of the algorithms—in the form of for example a deep neural network—and processing data By virtue of the generality of neural networks—they are general function approximators—training them is data hungry and typically requires large labeled training sets While benchmark training sets for object recognition store hundreds or thousands of examples per class label for many AI applications creating labeled training data is the most timeconsuming and expensive part of DL Learning to play video games may require hundreds of hours of training experience andor very expensive computing power In contrast writing an AI algorithm that covers every eventuality of a task to solve say reasoning about data and knowledge to label data automatically Ratner et al 2016 Roth 2017 and in turn make for example DL less datahungry–is a lot of manual work but we know what the algorithm does by design and that it can study and that it can more easily understand the complexity of the problem it solves When a machine has to interact with a human this seems to be especially valuableThis illustrates that ML and AI are indeed similar but not quite the same Artificial intelligence is about problem solving reasoning and learning in general Machine learning is specifically about learning—learning from examples from definitions from being told and from behavior The easiest way to think of their relationship is to visualize them as concentric circles with AI first and ML sitting inside with DL fitting inside both since ML also requires writing algorithms that cover every eventuality namely of the learning process The crucial point is that they share the idea of using computation as the language for intelligent behavior What kind of computation is used and how should it be programed This is not the right question Computation neither rules out search logical probabilistic and constraint programming techniques nor deep unsupervised and reinforcement learning methods among others but does as a computational model contain all of these techniquesReconsidering AlphaGo AlphaGo and its successor AlphaGo Zero Silver et al 2017 both combine DL and tree search—ML and AI Alternatively the “Allen AI Science Challenge” Schoenick et al 2017 should be considered The task was to comprehend a paragraph that states a science problem at the middle school level and then to answer a multiplechoice question All winning models employed ML yet failed to pass the test at the level of a competent middle schooler All winners argued that it was clear that applying a deeper semantic level of reasoning with scientific knowledge to the question and answers is the key to achieving true intelligence In other words AI has to cover knowledge reasoning and learning using programmed and learningbased programmed models in a combined fashion'"
+            ]
+          },
+          "execution_count": 192,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SezDmxICDsPo"
+      },
+      "source": [
+        "# Remove stopwords"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "90XqGTNhDh4A",
+        "outputId": "a9f0e4e4-2344-4757-a353-cc18bfe73906"
+      },
+      "source": [
+        "process_text=[word for word in nopunct.split() if word.lower() not in nltk.corpus.stopwords.words()]\n",
+        "process_text"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['Big',\n",
+              " 'Data',\n",
+              " 'Boosting',\n",
+              " 'Intelligent',\n",
+              " 'Behavior',\n",
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+              " 'learning',\n",
+              " 'ML',\n",
+              " 'artificial',\n",
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+              " 'becoming',\n",
+              " 'dominant',\n",
+              " 'problemsolving',\n",
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+              " 'areas',\n",
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+              " 'industry',\n",
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+              " 'news',\n",
+              " 'blogs',\n",
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+              " 'size',\n",
+              " 'data',\n",
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+              " 'across',\n",
+              " 'globe',\n",
+              " 'Data',\n",
+              " 'becoming',\n",
+              " 'meaningful',\n",
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+              " 'relevant',\n",
+              " 'breaking',\n",
+              " 'new',\n",
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+              " 'machine',\n",
+              " 'learning',\n",
+              " 'ML',\n",
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+              " 'deep',\n",
+              " 'learning',\n",
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+              " 'moving',\n",
+              " 'research',\n",
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+              " 'Jordan',\n",
+              " 'Mitchell',\n",
+              " '2015',\n",
+              " 'problem',\n",
+              " 'shifted',\n",
+              " 'collecting',\n",
+              " 'massive',\n",
+              " 'amounts',\n",
+              " 'data',\n",
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+              " 'knowledge',\n",
+              " 'conclusions',\n",
+              " 'actions',\n",
+              " 'Multiple',\n",
+              " 'research',\n",
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+              " 'cognitive',\n",
+              " 'sciences',\n",
+              " 'biology',\n",
+              " 'finance',\n",
+              " 'physics',\n",
+              " 'social',\n",
+              " 'sciences',\n",
+              " 'well',\n",
+              " 'many',\n",
+              " 'companies',\n",
+              " 'believe',\n",
+              " 'datadriven',\n",
+              " '“intelligent”',\n",
+              " 'solutions',\n",
+              " 'necessary',\n",
+              " 'solve',\n",
+              " 'many',\n",
+              " 'key',\n",
+              " 'problems',\n",
+              " 'Highthroughput',\n",
+              " 'genomic',\n",
+              " 'proteomic',\n",
+              " 'experiments',\n",
+              " 'used',\n",
+              " 'enable',\n",
+              " 'personalized',\n",
+              " 'medicine',\n",
+              " 'Large',\n",
+              " 'data',\n",
+              " 'sets',\n",
+              " 'search',\n",
+              " 'queries',\n",
+              " 'used',\n",
+              " 'improve',\n",
+              " 'information',\n",
+              " 'retrieval',\n",
+              " 'Historical',\n",
+              " 'climate',\n",
+              " 'data',\n",
+              " 'used',\n",
+              " 'understand',\n",
+              " 'global',\n",
+              " 'warming',\n",
+              " 'better',\n",
+              " 'predict',\n",
+              " 'weather',\n",
+              " 'Large',\n",
+              " 'amounts',\n",
+              " 'sensor',\n",
+              " 'readings',\n",
+              " 'hyperspectral',\n",
+              " 'images',\n",
+              " 'plants',\n",
+              " 'used',\n",
+              " 'identify',\n",
+              " 'drought',\n",
+              " 'conditions',\n",
+              " 'gain',\n",
+              " 'insights',\n",
+              " 'stress',\n",
+              " 'impacts',\n",
+              " 'plant',\n",
+              " 'growth',\n",
+              " 'development',\n",
+              " 'turn',\n",
+              " 'counterattack',\n",
+              " 'problem',\n",
+              " 'world',\n",
+              " 'hunger',\n",
+              " 'Game',\n",
+              " 'data',\n",
+              " 'turn',\n",
+              " 'pixels',\n",
+              " 'actions',\n",
+              " 'within',\n",
+              " 'video',\n",
+              " 'games',\n",
+              " 'observational',\n",
+              " 'data',\n",
+              " 'help',\n",
+              " 'enable',\n",
+              " 'robots',\n",
+              " 'understand',\n",
+              " 'complex',\n",
+              " 'unstructured',\n",
+              " 'environments',\n",
+              " 'learn',\n",
+              " 'manipulation',\n",
+              " 'skillsHowever',\n",
+              " 'ML',\n",
+              " 'DL',\n",
+              " 'really',\n",
+              " 'synonymous',\n",
+              " 'recently',\n",
+              " 'suggested',\n",
+              " 'news',\n",
+              " 'blogs',\n",
+              " 'media',\n",
+              " 'example',\n",
+              " 'AlphaGo',\n",
+              " 'Silver',\n",
+              " '2016',\n",
+              " 'defeated',\n",
+              " 'South',\n",
+              " 'Korean',\n",
+              " 'Master',\n",
+              " 'Lee',\n",
+              " 'Sedol',\n",
+              " 'board',\n",
+              " 'game',\n",
+              " 'Go',\n",
+              " '2016',\n",
+              " 'terms',\n",
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+              " 'AlphaGo',\n",
+              " 'addition',\n",
+              " 'even',\n",
+              " 'Gartners',\n",
+              " 'list',\n",
+              " 'Panetta',\n",
+              " '2017',\n",
+              " 'top',\n",
+              " '10',\n",
+              " 'Strategic',\n",
+              " 'Trends',\n",
+              " '2018',\n",
+              " 'places',\n",
+              " 'narrow',\n",
+              " 'top',\n",
+              " 'specifying',\n",
+              " '“consisting',\n",
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+              " 'machinelearning',\n",
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+              " 'Artificial',\n",
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+              " 'Machine',\n",
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+              " 'ML',\n",
+              " 'much',\n",
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+              " 'According',\n",
+              " 'McCarthy',\n",
+              " '2007',\n",
+              " 'founders',\n",
+              " 'fieldAI',\n",
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+              " 'fairly',\n",
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+              " 'abstractly',\n",
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+              " 'around',\n",
+              " 'world',\n",
+              " 'recognizing',\n",
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+              " 'sounds',\n",
+              " 'speaking',\n",
+              " 'translating',\n",
+              " 'performing',\n",
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+              " 'transactions',\n",
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+              " 'machine',\n",
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+              " 'affected',\n",
+              " '“body”',\n",
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+              " 'learns',\n",
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+              " 'MLML',\n",
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+              " 'construct',\n",
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+              " '2016',\n",
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+              " 'remarkable',\n",
+              " 'gains',\n",
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+              " 'domains',\n",
+              " 'spanning',\n",
+              " 'object',\n",
+              " 'recognition',\n",
+              " 'speech',\n",
+              " 'recognition',\n",
+              " 'control',\n",
+              " 'viewed',\n",
+              " 'constructing',\n",
+              " 'computer',\n",
+              " 'programs',\n",
+              " 'namely',\n",
+              " 'programming',\n",
+              " 'layers',\n",
+              " 'abstraction',\n",
+              " 'differentiable',\n",
+              " 'way',\n",
+              " 'using',\n",
+              " 'reusable',\n",
+              " 'structures',\n",
+              " 'convolution',\n",
+              " 'pooling',\n",
+              " 'auto',\n",
+              " 'encoders',\n",
+              " 'variational',\n",
+              " 'inference',\n",
+              " 'networks',\n",
+              " 'words',\n",
+              " 'replace',\n",
+              " 'complexity',\n",
+              " 'writing',\n",
+              " 'algorithms',\n",
+              " 'cover',\n",
+              " 'every',\n",
+              " 'eventuality',\n",
+              " 'complexity',\n",
+              " 'finding',\n",
+              " 'right',\n",
+              " 'general',\n",
+              " 'outline',\n",
+              " 'algorithms—in',\n",
+              " 'form',\n",
+              " 'example',\n",
+              " 'deep',\n",
+              " 'neural',\n",
+              " 'network—and',\n",
+              " 'processing',\n",
+              " 'data',\n",
+              " 'virtue',\n",
+              " 'generality',\n",
+              " 'neural',\n",
+              " 'networks—they',\n",
+              " 'general',\n",
+              " 'function',\n",
+              " 'approximators—training',\n",
+              " 'data',\n",
+              " 'hungry',\n",
+              " 'typically',\n",
+              " 'requires',\n",
+              " 'large',\n",
+              " 'labeled',\n",
+              " 'training',\n",
+              " 'sets',\n",
+              " 'benchmark',\n",
+              " 'training',\n",
+              " 'sets',\n",
+              " 'object',\n",
+              " 'recognition',\n",
+              " 'store',\n",
+              " 'hundreds',\n",
+              " 'thousands',\n",
+              " 'examples',\n",
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+              " 'many',\n",
+              " 'applications',\n",
+              " 'creating',\n",
+              " 'labeled',\n",
+              " 'training',\n",
+              " 'data',\n",
+              " 'timeconsuming',\n",
+              " 'expensive',\n",
+              " 'part',\n",
+              " 'DL',\n",
+              " 'Learning',\n",
+              " 'play',\n",
+              " 'video',\n",
+              " 'games',\n",
+              " 'may',\n",
+              " 'require',\n",
+              " 'hundreds',\n",
+              " 'hours',\n",
+              " 'training',\n",
+              " 'experience',\n",
+              " 'andor',\n",
+              " 'expensive',\n",
+              " 'computing',\n",
+              " 'power',\n",
+              " 'contrast',\n",
+              " 'writing',\n",
+              " 'algorithm',\n",
+              " 'covers',\n",
+              " 'every',\n",
+              " 'eventuality',\n",
+              " 'task',\n",
+              " 'solve',\n",
+              " 'say',\n",
+              " 'reasoning',\n",
+              " 'data',\n",
+              " 'knowledge',\n",
+              " 'label',\n",
+              " 'data',\n",
+              " 'automatically',\n",
+              " 'Ratner',\n",
+              " '2016',\n",
+              " 'Roth',\n",
+              " '2017',\n",
+              " 'turn',\n",
+              " 'make',\n",
+              " 'example',\n",
+              " 'DL',\n",
+              " 'less',\n",
+              " 'datahungry–is',\n",
+              " 'lot',\n",
+              " 'manual',\n",
+              " 'work',\n",
+              " 'know',\n",
+              " 'algorithm',\n",
+              " 'design',\n",
+              " 'study',\n",
+              " 'easily',\n",
+              " 'understand',\n",
+              " 'complexity',\n",
+              " 'problem',\n",
+              " 'solves',\n",
+              " 'machine',\n",
+              " 'interact',\n",
+              " 'human',\n",
+              " 'seems',\n",
+              " 'especially',\n",
+              " 'valuableThis',\n",
+              " 'illustrates',\n",
+              " 'ML',\n",
+              " 'indeed',\n",
+              " 'similar',\n",
+              " 'quite',\n",
+              " 'Artificial',\n",
+              " 'intelligence',\n",
+              " 'problem',\n",
+              " 'solving',\n",
+              " 'reasoning',\n",
+              " 'learning',\n",
+              " 'general',\n",
+              " 'Machine',\n",
+              " 'learning',\n",
+              " 'specifically',\n",
+              " 'learning—learning',\n",
+              " 'examples',\n",
+              " 'definitions',\n",
+              " 'told',\n",
+              " 'behavior',\n",
+              " 'easiest',\n",
+              " 'way',\n",
+              " 'think',\n",
+              " 'relationship',\n",
+              " 'visualize',\n",
+              " 'concentric',\n",
+              " 'circles',\n",
+              " 'first',\n",
+              " 'ML',\n",
+              " 'sitting',\n",
+              " 'inside',\n",
+              " 'DL',\n",
+              " 'fitting',\n",
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+              " 'requires',\n",
+              " 'writing',\n",
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+              " 'eventuality',\n",
+              " 'namely',\n",
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+              " 'idea',\n",
+              " 'using',\n",
+              " 'computation',\n",
+              " 'language',\n",
+              " 'intelligent',\n",
+              " 'behavior',\n",
+              " 'kind',\n",
+              " 'computation',\n",
+              " 'used',\n",
+              " 'programed',\n",
+              " 'right',\n",
+              " 'question',\n",
+              " 'Computation',\n",
+              " 'neither',\n",
+              " 'rules',\n",
+              " 'search',\n",
+              " 'logical',\n",
+              " 'probabilistic',\n",
+              " 'constraint',\n",
+              " 'programming',\n",
+              " 'techniques',\n",
+              " 'deep',\n",
+              " 'unsupervised',\n",
+              " 'reinforcement',\n",
+              " 'learning',\n",
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+              " 'among',\n",
+              " 'others',\n",
+              " 'computational',\n",
+              " 'model',\n",
+              " 'contain',\n",
+              " 'techniquesReconsidering',\n",
+              " 'AlphaGo',\n",
+              " 'AlphaGo',\n",
+              " 'successor',\n",
+              " 'AlphaGo',\n",
+              " 'Zero',\n",
+              " 'Silver',\n",
+              " '2017',\n",
+              " 'combine',\n",
+              " 'DL',\n",
+              " 'tree',\n",
+              " 'search—ML',\n",
+              " 'Alternatively',\n",
+              " '“Allen',\n",
+              " 'Science',\n",
+              " 'Challenge”',\n",
+              " 'Schoenick',\n",
+              " '2017',\n",
+              " 'considered',\n",
+              " 'task',\n",
+              " 'comprehend',\n",
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+              " 'middle',\n",
+              " 'school',\n",
+              " 'level',\n",
+              " 'answer',\n",
+              " 'multiplechoice',\n",
+              " 'question',\n",
+              " 'winning',\n",
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+              " 'failed',\n",
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+              " 'test',\n",
+              " 'level',\n",
+              " 'competent',\n",
+              " 'middle',\n",
+              " 'schooler',\n",
+              " 'winners',\n",
+              " 'argued',\n",
+              " 'clear',\n",
+              " 'applying',\n",
+              " 'deeper',\n",
+              " 'semantic',\n",
+              " 'level',\n",
+              " 'reasoning',\n",
+              " 'scientific',\n",
+              " 'knowledge',\n",
+              " 'question',\n",
+              " 'answers',\n",
+              " 'key',\n",
+              " 'achieving',\n",
+              " 'true',\n",
+              " 'intelligence',\n",
+              " 'words',\n",
+              " 'cover',\n",
+              " 'knowledge',\n",
+              " 'reasoning',\n",
+              " 'learning',\n",
+              " 'using',\n",
+              " 'programmed',\n",
+              " 'learningbased',\n",
+              " 'programmed',\n",
+              " 'models',\n",
+              " 'combined',\n",
+              " 'fashion']"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fsdv61XtINFH",
+        "outputId": "8389a937-fb0c-4143-e806-c513cae61138"
+      },
+      "source": [
+        "word_freq={}\n",
+        "for word in process_text:\n",
+        "  if word not in word_freq:\n",
+        "    word_freq[word]=1\n",
+        "  else:\n",
+        "      word_freq[word]=word_freq[word]+1\n",
+        "\n",
+        "dict(sorted(word_freq.items(),key=lambda item:item[1],reverse=True))\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'10': 1,\n",
+              " '1997So': 1,\n",
+              " '2007': 1,\n",
+              " '2015': 2,\n",
+              " '2016': 4,\n",
+              " '2017': 4,\n",
+              " '2018': 1,\n",
+              " 'AIMLDL': 1,\n",
+              " 'According': 1,\n",
+              " 'AlphaGo': 5,\n",
+              " 'Alternatively': 1,\n",
+              " 'Artificial': 2,\n",
+              " 'Behavior': 1,\n",
+              " 'Big': 1,\n",
+              " 'Boosting': 1,\n",
+              " 'Challenge”': 1,\n",
+              " 'Computation': 2,\n",
+              " 'DL': 9,\n",
+              " 'Data': 3,\n",
+              " 'Deep': 1,\n",
+              " 'Game': 1,\n",
+              " 'Gartners': 1,\n",
+              " 'Go': 1,\n",
+              " 'Goodfellow': 1,\n",
+              " 'Highthroughput': 1,\n",
+              " 'Historical': 1,\n",
+              " 'However': 1,\n",
+              " 'Intelligence': 1,\n",
+              " 'Intelligent': 1,\n",
+              " 'Jordan': 1,\n",
+              " 'Korean': 1,\n",
+              " 'Large': 2,\n",
+              " 'LeCun': 1,\n",
+              " 'Learning': 1,\n",
+              " 'LearningArtificial': 1,\n",
+              " 'Lee': 1,\n",
+              " 'ML': 11,\n",
+              " 'MLML': 1,\n",
+              " 'Machine': 2,\n",
+              " 'MachinesMachine': 1,\n",
+              " 'Master': 1,\n",
+              " 'McCarthy': 1,\n",
+              " 'Mitchell': 2,\n",
+              " 'Moreover': 1,\n",
+              " 'Multiple': 1,\n",
+              " 'Panetta': 1,\n",
+              " 'Ratner': 1,\n",
+              " 'Roth': 1,\n",
+              " 'Schoenick': 1,\n",
+              " 'Science': 1,\n",
+              " 'Sedol': 1,\n",
+              " 'Silver': 2,\n",
+              " 'South': 1,\n",
+              " 'Strategic': 1,\n",
+              " 'Trends': 1,\n",
+              " 'Zero': 1,\n",
+              " 'abstraction': 1,\n",
+              " 'abstractly': 1,\n",
+              " 'achieve': 1,\n",
+              " 'achieved': 1,\n",
+              " 'achieving': 1,\n",
+              " 'across': 1,\n",
+              " 'actions': 2,\n",
+              " 'addition': 1,\n",
+              " 'affected': 1,\n",
+              " 'algorithm': 2,\n",
+              " 'algorithms': 2,\n",
+              " 'algorithms—in': 1,\n",
+              " 'among': 2,\n",
+              " 'amounts': 2,\n",
+              " 'andor': 1,\n",
+              " 'answer': 1,\n",
+              " 'answers': 1,\n",
+              " 'applications': 1,\n",
+              " 'applying': 1,\n",
+              " 'approximators—training': 1,\n",
+              " 'areas': 1,\n",
+              " 'argued': 1,\n",
+              " 'around': 1,\n",
+              " 'art': 1,\n",
+              " 'artificial': 2,\n",
+              " 'auto': 1,\n",
+              " 'automatically': 3,\n",
+              " 'becoming': 2,\n",
+              " 'behavior': 5,\n",
+              " 'believe': 1,\n",
+              " 'benchmark': 1,\n",
+              " 'better': 1,\n",
+              " 'biologically': 1,\n",
+              " 'biology': 1,\n",
+              " 'blogs': 2,\n",
+              " 'board': 1,\n",
+              " 'breaking': 1,\n",
+              " 'breakthrough': 1,\n",
+              " 'business': 1,\n",
+              " 'circles': 1,\n",
+              " 'class': 1,\n",
+              " 'clear': 1,\n",
+              " 'clever': 1,\n",
+              " 'climate': 1,\n",
+              " 'cognitive': 1,\n",
+              " 'collected': 1,\n",
+              " 'collecting': 1,\n",
+              " 'combine': 1,\n",
+              " 'combined': 1,\n",
+              " 'combining': 1,\n",
+              " 'companies': 1,\n",
+              " 'competent': 1,\n",
+              " 'complement': 1,\n",
+              " 'complex': 1,\n",
+              " 'complexity': 3,\n",
+              " 'comprehend': 1,\n",
+              " 'computation': 4,\n",
+              " 'computational': 2,\n",
+              " 'computer': 4,\n",
+              " 'computers': 1,\n",
+              " 'computing': 1,\n",
+              " 'concentric': 1,\n",
+              " 'conclusions': 1,\n",
+              " 'conditions': 1,\n",
+              " 'confine': 1,\n",
+              " 'considered': 1,\n",
+              " 'constraint': 1,\n",
+              " 'construct': 1,\n",
+              " 'constructing': 2,\n",
+              " 'contain': 1,\n",
+              " 'contextually': 1,\n",
+              " 'contrast': 1,\n",
+              " 'control': 1,\n",
+              " 'controlling': 1,\n",
+              " 'convolution': 1,\n",
+              " 'counterattack': 1,\n",
+              " 'cover': 3,\n",
+              " 'covers': 1,\n",
+              " 'creating': 2,\n",
+              " 'creative': 1,\n",
+              " 'crucial': 1,\n",
+              " 'data': 12,\n",
+              " 'datadriven': 1,\n",
+              " 'datahungry–is': 1,\n",
+              " 'deep': 5,\n",
+              " 'deeper': 1,\n",
+              " 'defeated': 1,\n",
+              " 'definitions': 1,\n",
+              " 'describe': 1,\n",
+              " 'design': 1,\n",
+              " 'development': 1,\n",
+              " 'differentiable': 1,\n",
+              " 'disciplines': 1,\n",
+              " 'domains': 1,\n",
+              " 'dominant': 1,\n",
+              " 'drought': 1,\n",
+              " 'easiest': 1,\n",
+              " 'easily': 1,\n",
+              " 'embedded': 1,\n",
+              " 'employed': 1,\n",
+              " 'enable': 2,\n",
+              " 'encoders': 1,\n",
+              " 'engineering': 1,\n",
+              " 'enviroment': 1,\n",
+              " 'environments': 1,\n",
+              " 'equation': 1,\n",
+              " 'especially': 2,\n",
+              " 'even': 1,\n",
+              " 'eventuality': 3,\n",
+              " 'every': 3,\n",
+              " 'example': 3,\n",
+              " 'examples': 2,\n",
+              " 'exception': 1,\n",
+              " 'expensive': 2,\n",
+              " 'experience': 1,\n",
+              " 'experience”': 1,\n",
+              " 'experiments': 1,\n",
+              " 'exponential': 1,\n",
+              " 'fad': 1,\n",
+              " 'failed': 1,\n",
+              " 'fairly': 1,\n",
+              " 'falls': 1,\n",
+              " 'fashion': 1,\n",
+              " 'fieldAI': 1,\n",
+              " 'fields': 1,\n",
+              " 'finance': 1,\n",
+              " 'finding': 1,\n",
+              " 'first': 1,\n",
+              " 'fitting': 1,\n",
+              " 'form': 1,\n",
+              " 'founders': 1,\n",
+              " 'function': 1,\n",
+              " 'fundamental': 1,\n",
+              " 'gain': 1,\n",
+              " 'gains': 1,\n",
+              " 'game': 1,\n",
+              " 'games': 2,\n",
+              " 'general': 3,\n",
+              " 'generality': 1,\n",
+              " 'generalizing': 1,\n",
+              " 'generic': 1,\n",
+              " 'genomic': 1,\n",
+              " 'global': 1,\n",
+              " 'globe': 1,\n",
+              " 'goals': 1,\n",
+              " 'grounds': 1,\n",
+              " 'growing': 1,\n",
+              " 'growth': 1,\n",
+              " 'help': 1,\n",
+              " 'highly': 1,\n",
+              " 'hours': 1,\n",
+              " 'however': 1,\n",
+              " 'human': 2,\n",
+              " 'humanlike': 1,\n",
+              " 'hundreds': 2,\n",
+              " 'hunger': 1,\n",
+              " 'hungry': 1,\n",
+              " 'hyperspectral': 1,\n",
+              " 'hypotheses': 1,\n",
+              " 'idea': 1,\n",
+              " 'identify': 1,\n",
+              " 'illustrates': 1,\n",
+              " 'images': 1,\n",
+              " 'impacts': 1,\n",
+              " 'improve': 2,\n",
+              " 'include': 1,\n",
+              " 'includes': 1,\n",
+              " 'indeed': 1,\n",
+              " 'industry': 1,\n",
+              " 'inference': 1,\n",
+              " 'information': 1,\n",
+              " 'inside': 2,\n",
+              " 'insights': 1,\n",
+              " 'instance': 1,\n",
+              " 'intelligence': 6,\n",
+              " 'intelligent': 5,\n",
+              " 'interact': 1,\n",
+              " 'it—turning': 1,\n",
+              " 'keep': 1,\n",
+              " 'key': 2,\n",
+              " 'kind': 2,\n",
+              " 'know': 1,\n",
+              " 'knowledge': 4,\n",
+              " 'label': 2,\n",
+              " 'labeled': 2,\n",
+              " 'labs': 1,\n",
+              " 'language': 1,\n",
+              " 'large': 1,\n",
+              " 'layers': 1,\n",
+              " 'learn': 1,\n",
+              " 'learning': 11,\n",
+              " 'learningbased': 1,\n",
+              " 'learning—learning': 1,\n",
+              " 'learns': 1,\n",
+              " 'least': 1,\n",
+              " 'less': 1,\n",
+              " 'level': 3,\n",
+              " 'lies': 1,\n",
+              " 'list': 1,\n",
+              " 'logical': 2,\n",
+              " 'lot': 1,\n",
+              " 'machine': 3,\n",
+              " 'machinelearning': 1,\n",
+              " 'machines': 2,\n",
+              " 'make': 1,\n",
+              " 'making': 1,\n",
+              " 'manipulation': 1,\n",
+              " 'manual': 1,\n",
+              " 'many': 5,\n",
+              " 'massive': 1,\n",
+              " 'may': 1,\n",
+              " 'meaningful': 1,\n",
+              " 'media': 3,\n",
+              " 'medicine': 1,\n",
+              " 'methods': 3,\n",
+              " 'middle': 2,\n",
+              " 'model': 2,\n",
+              " 'models': 3,\n",
+              " 'moving': 2,\n",
+              " 'much': 1,\n",
+              " 'multiple': 1,\n",
+              " 'multiplechoice': 1,\n",
+              " 'namely': 2,\n",
+              " 'narrow': 1,\n",
+              " 'necessary': 1,\n",
+              " 'neither': 2,\n",
+              " 'networks': 1,\n",
+              " 'networks—they': 1,\n",
+              " 'network—and': 1,\n",
+              " 'neural': 2,\n",
+              " 'new': 1,\n",
+              " 'news': 2,\n",
+              " 'next': 1,\n",
+              " 'object': 2,\n",
+              " 'objects': 1,\n",
+              " 'observable”This': 1,\n",
+              " 'observational': 1,\n",
+              " 'others': 2,\n",
+              " 'outcome': 1,\n",
+              " 'outline': 1,\n",
+              " 'paragraph': 1,\n",
+              " 'part': 1,\n",
+              " 'particular': 1,\n",
+              " 'pass': 1,\n",
+              " 'performing': 1,\n",
+              " 'personalized': 1,\n",
+              " 'physically': 1,\n",
+              " 'physics': 1,\n",
+              " 'pixels': 1,\n",
+              " 'places': 1,\n",
+              " 'planning': 1,\n",
+              " 'plant': 1,\n",
+              " 'plants': 1,\n",
+              " 'play': 1,\n",
+              " 'poetry': 1,\n",
+              " 'point': 1,\n",
+              " 'pooling': 1,\n",
+              " 'power': 1,\n",
+              " 'predict': 1,\n",
+              " 'probabilistic': 2,\n",
+              " 'problem': 5,\n",
+              " 'problems': 1,\n",
+              " 'problemsolving': 1,\n",
+              " 'process': 1,\n",
+              " 'processing': 1,\n",
+              " 'production': 1,\n",
+              " 'program': 3,\n",
+              " 'programed': 1,\n",
+              " 'programmed': 2,\n",
+              " 'programming': 2,\n",
+              " 'programs': 4,\n",
+              " 'proteomic': 1,\n",
+              " 'pushing': 1,\n",
+              " 'puzzles': 1,\n",
+              " 'queries': 1,\n",
+              " 'question': 5,\n",
+              " 'quite': 1,\n",
+              " 'rate': 1,\n",
+              " 'readings': 1,\n",
+              " 'really': 1,\n",
+              " 'reasoning': 5,\n",
+              " 'recent': 1,\n",
+              " 'recently': 2,\n",
+              " 'recognition': 3,\n",
+              " 'recognizing': 1,\n",
+              " 'reinforcement': 2,\n",
+              " 'related': 2,\n",
+              " 'relationship': 1,\n",
+              " 'relevant': 1,\n",
+              " 'remarkable': 1,\n",
+              " 'replace': 1,\n",
+              " 'require': 1,\n",
+              " 'requires': 2,\n",
+              " 'research': 3,\n",
+              " 'retrieval': 1,\n",
+              " 'reusable': 1,\n",
+              " 'right': 3,\n",
+              " 'robots': 2,\n",
+              " 'rules': 2,\n",
+              " 'say': 2,\n",
+              " 'school': 1,\n",
+              " 'schooler': 1,\n",
+              " 'science': 3,\n",
+              " 'sciences': 2,\n",
+              " 'scientific': 1,\n",
+              " 'scoped': 1,\n",
+              " 'search': 3,\n",
+              " 'search—ML': 1,\n",
+              " 'seems': 1,\n",
+              " 'semantic': 1,\n",
+              " 'sensor': 1,\n",
+              " 'sets': 3,\n",
+              " 'share': 2,\n",
+              " 'shifted': 1,\n",
+              " 'short': 1,\n",
+              " 'similar': 2,\n",
+              " 'simple': 1,\n",
+              " 'since': 1,\n",
+              " 'sitting': 1,\n",
+              " 'size': 1,\n",
+              " 'skillsHowever': 1,\n",
+              " 'social': 2,\n",
+              " 'solutions': 2,\n",
+              " 'solve': 2,\n",
+              " 'solves': 1,\n",
+              " 'solving': 2,\n",
+              " 'sounds': 1,\n",
+              " 'spanning': 1,\n",
+              " 'speaking': 1,\n",
+              " 'specific': 1,\n",
+              " 'specifically': 1,\n",
+              " 'specifying': 1,\n",
+              " 'speech': 1,\n",
+              " 'states': 1,\n",
+              " 'store': 1,\n",
+              " 'stress': 1,\n",
+              " 'structures': 1,\n",
+              " 'study': 1,\n",
+              " 'successes': 1,\n",
+              " 'successor': 1,\n",
+              " 'suggested': 2,\n",
+              " 'synonymous': 1,\n",
+              " 'target': 1,\n",
+              " 'task': 3,\n",
+              " 'tasks': 1,\n",
+              " 'task”2': 1,\n",
+              " 'techniques': 3,\n",
+              " 'techniquesReconsidering': 1,\n",
+              " 'terms': 1,\n",
+              " 'test': 1,\n",
+              " 'themBig': 1,\n",
+              " 'think': 1,\n",
+              " 'thousands': 1,\n",
+              " 'timeconsuming': 1,\n",
+              " 'told': 1,\n",
+              " 'top': 2,\n",
+              " 'training': 4,\n",
+              " 'transactions': 1,\n",
+              " 'translating': 1,\n",
+              " 'tree': 1,\n",
+              " 'true': 1,\n",
+              " 'turn': 3,\n",
+              " 'typically': 1,\n",
+              " 'understand': 4,\n",
+              " 'understanding': 1,\n",
+              " 'unless': 1,\n",
+              " 'unstructured': 1,\n",
+              " 'unsupervised': 2,\n",
+              " 'used': 6,\n",
+              " 'useful': 1,\n",
+              " 'using': 4,\n",
+              " 'valuableThis': 1,\n",
+              " 'variational': 1,\n",
+              " 'video': 2,\n",
+              " 'viewed': 1,\n",
+              " 'virtue': 1,\n",
+              " 'visualize': 1,\n",
+              " 'warming': 1,\n",
+              " 'way': 3,\n",
+              " 'weather': 1,\n",
+              " 'well': 1,\n",
+              " 'winners': 1,\n",
+              " 'winning': 1,\n",
+              " 'within': 1,\n",
+              " 'words': 2,\n",
+              " 'work': 2,\n",
+              " 'world': 4,\n",
+              " 'write': 1,\n",
+              " 'writing': 3,\n",
+              " 'yet': 1,\n",
+              " '“Allen': 1,\n",
+              " '“body”': 1,\n",
+              " '“concerned': 1,\n",
+              " '“consisting': 1,\n",
+              " '“intelligent”': 1,\n",
+              " '“the': 1}"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "C6rW7hKXO8P0",
+        "outputId": "d5abed92-2eee-4199-b5f5-af74bd9ae26a"
+      },
+      "source": [
+        "max_freq=max(word_freq.values())\n",
+        "\n",
+        "for word in word_freq.keys():\n",
+        "  word_freq[word]=(word_freq[word]/max_freq)\n",
+        "word_freq"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'10': 0.08333333333333333,\n",
+              " '1997So': 0.08333333333333333,\n",
+              " '2007': 0.08333333333333333,\n",
+              " '2015': 0.16666666666666666,\n",
+              " '2016': 0.3333333333333333,\n",
+              " '2017': 0.3333333333333333,\n",
+              " '2018': 0.08333333333333333,\n",
+              " 'AIMLDL': 0.08333333333333333,\n",
+              " 'According': 0.08333333333333333,\n",
+              " 'AlphaGo': 0.4166666666666667,\n",
+              " 'Alternatively': 0.08333333333333333,\n",
+              " 'Artificial': 0.16666666666666666,\n",
+              " 'Behavior': 0.08333333333333333,\n",
+              " 'Big': 0.08333333333333333,\n",
+              " 'Boosting': 0.08333333333333333,\n",
+              " 'Challenge”': 0.08333333333333333,\n",
+              " 'Computation': 0.16666666666666666,\n",
+              " 'DL': 0.75,\n",
+              " 'Data': 0.25,\n",
+              " 'Deep': 0.08333333333333333,\n",
+              " 'Game': 0.08333333333333333,\n",
+              " 'Gartners': 0.08333333333333333,\n",
+              " 'Go': 0.08333333333333333,\n",
+              " 'Goodfellow': 0.08333333333333333,\n",
+              " 'Highthroughput': 0.08333333333333333,\n",
+              " 'Historical': 0.08333333333333333,\n",
+              " 'However': 0.08333333333333333,\n",
+              " 'Intelligence': 0.08333333333333333,\n",
+              " 'Intelligent': 0.08333333333333333,\n",
+              " 'Jordan': 0.08333333333333333,\n",
+              " 'Korean': 0.08333333333333333,\n",
+              " 'Large': 0.16666666666666666,\n",
+              " 'LeCun': 0.08333333333333333,\n",
+              " 'Learning': 0.08333333333333333,\n",
+              " 'LearningArtificial': 0.08333333333333333,\n",
+              " 'Lee': 0.08333333333333333,\n",
+              " 'ML': 0.9166666666666666,\n",
+              " 'MLML': 0.08333333333333333,\n",
+              " 'Machine': 0.16666666666666666,\n",
+              " 'MachinesMachine': 0.08333333333333333,\n",
+              " 'Master': 0.08333333333333333,\n",
+              " 'McCarthy': 0.08333333333333333,\n",
+              " 'Mitchell': 0.16666666666666666,\n",
+              " 'Moreover': 0.08333333333333333,\n",
+              " 'Multiple': 0.08333333333333333,\n",
+              " 'Panetta': 0.08333333333333333,\n",
+              " 'Ratner': 0.08333333333333333,\n",
+              " 'Roth': 0.08333333333333333,\n",
+              " 'Schoenick': 0.08333333333333333,\n",
+              " 'Science': 0.08333333333333333,\n",
+              " 'Sedol': 0.08333333333333333,\n",
+              " 'Silver': 0.16666666666666666,\n",
+              " 'South': 0.08333333333333333,\n",
+              " 'Strategic': 0.08333333333333333,\n",
+              " 'Trends': 0.08333333333333333,\n",
+              " 'Zero': 0.08333333333333333,\n",
+              " 'abstraction': 0.08333333333333333,\n",
+              " 'abstractly': 0.08333333333333333,\n",
+              " 'achieve': 0.08333333333333333,\n",
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+              " 'achieving': 0.08333333333333333,\n",
+              " 'across': 0.08333333333333333,\n",
+              " 'actions': 0.16666666666666666,\n",
+              " 'addition': 0.08333333333333333,\n",
+              " 'affected': 0.08333333333333333,\n",
+              " 'algorithm': 0.16666666666666666,\n",
+              " 'algorithms': 0.16666666666666666,\n",
+              " 'algorithms—in': 0.08333333333333333,\n",
+              " 'among': 0.16666666666666666,\n",
+              " 'amounts': 0.16666666666666666,\n",
+              " 'andor': 0.08333333333333333,\n",
+              " 'answer': 0.08333333333333333,\n",
+              " 'answers': 0.08333333333333333,\n",
+              " 'applications': 0.08333333333333333,\n",
+              " 'applying': 0.08333333333333333,\n",
+              " 'approximators—training': 0.08333333333333333,\n",
+              " 'areas': 0.08333333333333333,\n",
+              " 'argued': 0.08333333333333333,\n",
+              " 'around': 0.08333333333333333,\n",
+              " 'art': 0.08333333333333333,\n",
+              " 'artificial': 0.16666666666666666,\n",
+              " 'auto': 0.08333333333333333,\n",
+              " 'automatically': 0.25,\n",
+              " 'becoming': 0.16666666666666666,\n",
+              " 'behavior': 0.4166666666666667,\n",
+              " 'believe': 0.08333333333333333,\n",
+              " 'benchmark': 0.08333333333333333,\n",
+              " 'better': 0.08333333333333333,\n",
+              " 'biologically': 0.08333333333333333,\n",
+              " 'biology': 0.08333333333333333,\n",
+              " 'blogs': 0.16666666666666666,\n",
+              " 'board': 0.08333333333333333,\n",
+              " 'breaking': 0.08333333333333333,\n",
+              " 'breakthrough': 0.08333333333333333,\n",
+              " 'business': 0.08333333333333333,\n",
+              " 'circles': 0.08333333333333333,\n",
+              " 'class': 0.08333333333333333,\n",
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+              " 'yet': 0.08333333333333333,\n",
+              " '“Allen': 0.08333333333333333,\n",
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+              " '“concerned': 0.08333333333333333,\n",
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+              " '“intelligent”': 0.08333333333333333,\n",
+              " '“the': 0.08333333333333333}"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "4tie1KxVD3p-"
+      },
+      "source": [
+        "# Tokenization"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3xYRApfaNcnA",
+        "outputId": "4eaad839-cb9c-4f80-89ce-518b9753b801"
+      },
+      "source": [
+        "sent_list=nltk.sent_tokenize(text)\n",
+        "sent_list\n",
+        "    "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['Big Data Is Boosting Intelligent Behavior in Machines\\nMachine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL).',\n",
+              " 'However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short.',\n",
+              " 'These fields share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines.',\n",
+              " 'What kind of computation and how to program it?',\n",
+              " 'This is not the right question.',\n",
+              " 'Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them.',\n",
+              " 'They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them.',\n",
+              " 'Big Data is no fad.',\n",
+              " 'The world is growing at an exponential rate and so is the size of the data collected across the globe.',\n",
+              " 'Data is becoming more meaningful and contextually relevant, breaking new grounds for machine learning (ML), in particular for deep learning (DL) and artificial intelligence (AI), moving them out of research labs into production (Jordan and Mitchell, 2015).',\n",
+              " 'The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge, conclusions, and actions.',\n",
+              " 'Multiple research disciplines, from cognitive sciences to biology, finance, physics, and social sciences, as well as many companies believe that data-driven and “intelligent” solutions are necessary to solve many of their key problems.',\n",
+              " 'High-throughput genomic and proteomic experiments can be used to enable personalized medicine.',\n",
+              " 'Large data sets of search queries can be used to improve information retrieval.',\n",
+              " 'Historical climate data can be used to understand global warming and to better predict weather.',\n",
+              " 'Large amounts of sensor readings and hyperspectral images of plants can be used to identify drought conditions and to gain insights into when and how stress impacts plant growth and development and in turn how to counterattack the problem of world hunger.',\n",
+              " 'Game data can turn pixels into actions within video games, while observational data can help enable robots to understand complex and unstructured environments and to learn manipulation skills.',\n",
+              " 'However, is AI, ML, and DL really synonymous, as recently suggested in the news, blogs, and media?',\n",
+              " 'For example, when AlphaGo (Silver et al., 2016) defeated South Korean Master Lee Se-dol in the board game Go in 2016, the terms AI, ML, and DL were used by the media to describe how AlphaGo won.',\n",
+              " \"In addition to this, even Gartner's list (Panetta, 2017) of top 10 Strategic Trends for 2018 places (narrow) AI at the very top, specifying it as “consisting of highly scoped machine-learning solutions that target a specific task.”\\n\\n2.\",\n",
+              " 'Artificial Intelligence and Machine Learning\\nArtificial intelligence and ML are very much related.',\n",
+              " 'According to McCarthy (2007), one of the founders of the field,\\n\\nAI is “the science and engineering of making intelligent machines, especially intelligent computer programs.',\n",
+              " 'It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”\\n\\nThis is fairly generic and includes multiple tasks such as abstractly reasoning and generalizing about the world, solving puzzles, planning how to achieve goals, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (e.g., creating art or poetry), and controlling robots.',\n",
+              " 'Moreover, the behavior of a machine is not just the outcome of the program, it is also affected by its “body” and the enviroment it is physically embedded in.',\n",
+              " 'To keep it simple, however, if you can write a very clever program that has, say, human-like behavior, it can be AI.',\n",
+              " 'But unless it automatically learns from data, it is not ML:\\n\\nML is the science that is “concerned with the question of how to construct computer programs that automatically improve with experience,” (Mitchell, 1997).',\n",
+              " 'So, AI and ML are both about constructing intelligent computer programs, and DL, being an instance of ML, is no exception.',\n",
+              " 'Deep learning (LeCun et al., 2015; Goodfellow et al., 2016), which has achieved remarkable gains in many domains spanning from object recognition, speech recognition, and control, can be viewed as constructing computer programs, namely programming layers of abstraction in a differentiable way using reusable structures such as convolution, pooling, auto encoders, variational inference networks, and so on.',\n",
+              " 'In other words, we replace the complexity of writing algorithms, that cover every eventuality, with the complexity of finding the right general outline of the algorithms—in the form of, for example, a deep neural network—and processing data.',\n",
+              " 'By virtue of the generality of neural networks—they are general function approximators—training them is data hungry and typically requires large labeled training sets.',\n",
+              " 'While benchmark training sets for object recognition, store hundreds or thousands of examples per class label, for many AI applications, creating labeled training data is the most time-consuming and expensive part of DL.',\n",
+              " 'Learning to play video games may require hundreds of hours of training experience and/or very expensive computing power.',\n",
+              " 'In contrast, writing an AI algorithm that covers every eventuality of a task to solve, say, reasoning about data and knowledge to label data automatically (Ratner et al., 2016; Roth, 2017) and, in turn, make, for example, DL less data-hungry–is a lot of manual work, but we know what the algorithm does by design and that it can study and that it can more easily understand the complexity of the problem it solves.',\n",
+              " 'When a machine has to interact with a human, this seems to be especially valuable.',\n",
+              " 'This illustrates that ML and AI are indeed similar, but not quite the same.',\n",
+              " 'Artificial intelligence is about problem solving, reasoning, and learning in general.',\n",
+              " 'Machine learning is specifically about learning—learning from examples, from definitions, from being told, and from behavior.',\n",
+              " 'The easiest way to think of their relationship is to visualize them as concentric circles with AI first and ML sitting inside (with DL fitting inside both), since ML also requires writing algorithms that cover every eventuality, namely, of the learning process.',\n",
+              " 'The crucial point is that they share the idea of using computation as the language for intelligent behavior.',\n",
+              " 'What kind of computation is used and how should it be programed?',\n",
+              " 'This is not the right question.',\n",
+              " 'Computation neither rules out search, logical, probabilistic, and constraint programming techniques nor (deep) (un)supervised and reinforcement learning methods, among others, but does, as a computational model, contain all of these techniques.',\n",
+              " 'Reconsidering AlphaGo: AlphaGo and its successor AlphaGo Zero (Silver et al., 2017) both combine DL and tree search—ML and AI.',\n",
+              " 'Alternatively, the “Allen AI Science Challenge” (Schoenick et al., 2017) should be considered.',\n",
+              " 'The task was to comprehend a paragraph that states a science problem, at the middle school level and then to answer a multiple-choice question.',\n",
+              " 'All winning models employed ML yet failed to pass the test at the level of a competent middle schooler.',\n",
+              " 'All winners argued that it was clear that applying a deeper, semantic level of reasoning with scientific knowledge to the question and answers, is the key to achieving true intelligence.',\n",
+              " 'In other words, AI has to cover knowledge, reasoning, and learning, using programmed and learning-based programmed models in a combined fashion.']"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UWjzfzgHl6TE",
+        "outputId": "9f377d96-a658-4427-b5b4-6ed1c4a9b554"
+      },
+      "source": [
+        "sent_score={}\n",
+        "\n",
+        "for sent in sent_list:\n",
+        "  for word in nltk.word_tokenize(sent.lower()):\n",
+        "    if word in word_freq.keys():\n",
+        "       if sent not in sent_score.keys():\n",
+        "         sent_score[sent]=word_freq[word]\n",
+        "       else:\n",
+        "         sent_score[sent]=sent_score[sent]+word_freq[word]\n",
+        "\n",
+        "dict(sorted(sent_score.items(),key=lambda item:item[1],reverse=True))  "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'According to McCarthy (2007), one of the founders of the field,\\n\\nAI is “the science and engineering of making intelligent machines, especially intelligent computer programs.': 2.416666666666667,\n",
+              " 'All winners argued that it was clear that applying a deeper, semantic level of reasoning with scientific knowledge to the question and answers, is the key to achieving true intelligence.': 2.916666666666667,\n",
+              " 'All winning models employed ML yet failed to pass the test at the level of a competent middle schooler.': 1.3333333333333333,\n",
+              " 'Alternatively, the “Allen AI Science Challenge” (Schoenick et al., 2017) should be considered.': 0.6666666666666666,\n",
+              " 'Artificial Intelligence and Machine Learning\\nArtificial intelligence and ML are very much related.': 2.75,\n",
+              " 'Artificial intelligence is about problem solving, reasoning, and learning in general.': 2.8333333333333335,\n",
+              " 'Big Data Is Boosting Intelligent Behavior in Machines\\nMachine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL).': 6.749999999999999,\n",
+              " 'Big Data is no fad.': 1.0833333333333333,\n",
+              " 'But unless it automatically learns from data, it is not ML:\\n\\nML is the science that is “concerned with the question of how to construct computer programs that automatically improve with experience,” (Mitchell, 1997).': 3.3333333333333335,\n",
+              " 'By virtue of the generality of neural networks—they are general function approximators—training them is data hungry and typically requires large labeled training sets.': 3.0,\n",
+              " 'Computation neither rules out search, logical, and probabilistic techniques, nor (deep) (un)supervised and reinforcement learning methods, among others, as computational models do include all of them.': 4.083333333333333,\n",
+              " 'Computation neither rules out search, logical, probabilistic, and constraint programming techniques nor (deep) (un)supervised and reinforcement learning methods, among others, but does, as a computational model, contain all of these techniques.': 4.499999999999999,\n",
+              " 'Data is becoming more meaningful and contextually relevant, breaking new grounds for machine learning (ML), in particular for deep learning (DL) and artificial intelligence (AI), moving them out of research labs into production (Jordan and Mitchell, 2015).': 5.666666666666667,\n",
+              " 'Deep learning (LeCun et al., 2015; Goodfellow et al., 2016), which has achieved remarkable gains in many domains spanning from object recognition, speech recognition, and control, can be viewed as constructing computer programs, namely programming layers of abstraction in a differentiable way using reusable structures such as convolution, pooling, auto encoders, variational inference networks, and so on.': 6.3333333333333295,\n",
+              " 'For example, when AlphaGo (Silver et al., 2016) defeated South Korean Master Lee Se-dol in the board game Go in 2016, the terms AI, ML, and DL were used by the media to describe how AlphaGo won.': 2.0833333333333335,\n",
+              " 'Game data can turn pixels into actions within video games, while observational data can help enable robots to understand complex and unstructured environments and to learn manipulation skills.': 4.249999999999999,\n",
+              " 'High-throughput genomic and proteomic experiments can be used to enable personalized medicine.': 1.0833333333333333,\n",
+              " 'Historical climate data can be used to understand global warming and to better predict weather.': 2.3333333333333335,\n",
+              " 'However, is AI, ML, and DL really synonymous, as recently suggested in the news, blogs, and media?': 1.1666666666666665,\n",
+              " 'However, the equation AI=ML=DL, as recently suggested in the news, blogs, and media, falls too short.': 1.2499999999999998,\n",
+              " \"In addition to this, even Gartner's list (Panetta, 2017) of top 10 Strategic Trends for 2018 places (narrow) AI at the very top, specifying it as “consisting of highly scoped machine-learning solutions that target a specific task.”\\n\\n2.\": 1.833333333333333,\n",
+              " 'In contrast, writing an AI algorithm that covers every eventuality of a task to solve, say, reasoning about data and knowledge to label data automatically (Ratner et al., 2016; Roth, 2017) and, in turn, make, for example, DL less data-hungry–is a lot of manual work, but we know what the algorithm does by design and that it can study and that it can more easily understand the complexity of the problem it solves.': 8.083333333333332,\n",
+              " 'In other words, AI has to cover knowledge, reasoning, and learning, using programmed and learning-based programmed models in a combined fashion.': 3.166666666666667,\n",
+              " 'In other words, we replace the complexity of writing algorithms, that cover every eventuality, with the complexity of finding the right general outline of the algorithms—in the form of, for example, a deep neural network—and processing data.': 4.75,\n",
+              " 'It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”\\n\\nThis is fairly generic and includes multiple tasks such as abstractly reasoning and generalizing about the world, solving puzzles, planning how to achieve goals, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (e.g., creating art or poetry), and controlling robots.': 6.499999999999997,\n",
+              " 'Large amounts of sensor readings and hyperspectral images of plants can be used to identify drought conditions and to gain insights into when and how stress impacts plant growth and development and in turn how to counterattack the problem of world hunger.': 3.166666666666666,\n",
+              " 'Large data sets of search queries can be used to improve information retrieval.': 2.5,\n",
+              " 'Learning to play video games may require hundreds of hours of training experience and/or very expensive computing power.': 2.5000000000000004,\n",
+              " 'Machine learning is specifically about learning—learning from examples, from definitions, from being told, and from behavior.': 2.083333333333333,\n",
+              " 'Moreover, the behavior of a machine is not just the outcome of the program, it is also affected by its “body” and the enviroment it is physically embedded in.': 1.333333333333333,\n",
+              " 'Multiple research disciplines, from cognitive sciences to biology, finance, physics, and social sciences, as well as many companies believe that data-driven and “intelligent” solutions are necessary to solve many of their key problems.': 3.4166666666666665,\n",
+              " 'Reconsidering AlphaGo: AlphaGo and its successor AlphaGo Zero (Silver et al., 2017) both combine DL and tree search—ML and AI.': 0.5833333333333333,\n",
+              " 'So, AI and ML are both about constructing intelligent computer programs, and DL, being an instance of ML, is no exception.': 1.4166666666666665,\n",
+              " 'The crucial point is that they share the idea of using computation as the language for intelligent behavior.': 2.0,\n",
+              " 'The easiest way to think of their relationship is to visualize them as concentric circles with AI first and ML sitting inside (with DL fitting inside both), since ML also requires writing algorithms that cover every eventuality, namely, of the learning process.': 3.9166666666666665,\n",
+              " 'The problem has shifted from collecting massive amounts of data to understanding it—turning it into knowledge, conclusions, and actions.': 2.5833333333333335,\n",
+              " 'The task was to comprehend a paragraph that states a science problem, at the middle school level and then to answer a multiple-choice question.': 2.1666666666666665,\n",
+              " 'The world is growing at an exponential rate and so is the size of the data collected across the globe.': 1.9166666666666663,\n",
+              " 'These fields share the same fundamental hypotheses: computation is a useful way to model intelligent behavior in machines.': 2.25,\n",
+              " 'They complement each other, and the next breakthrough lies not only in pushing each of them but also in combining them.': 0.49999999999999994,\n",
+              " 'This illustrates that ML and AI are indeed similar, but not quite the same.': 0.41666666666666663,\n",
+              " 'This is not the right question.': 1.3333333333333335,\n",
+              " 'To keep it simple, however, if you can write a very clever program that has, say, human-like behavior, it can be AI.': 1.25,\n",
+              " 'What kind of computation and how to program it?': 0.75,\n",
+              " 'What kind of computation is used and how should it be programed?': 1.0833333333333333,\n",
+              " 'When a machine has to interact with a human, this seems to be especially valuable.': 0.75,\n",
+              " 'While benchmark training sets for object recognition, store hundreds or thousands of examples per class label, for many AI applications, creating labeled training data is the most time-consuming and expensive part of DL.': 4.25}"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xw3zTuv0AgRe",
+        "outputId": "33185864-bddc-4130-cccf-93a35fb7eca6"
+      },
+      "source": [
+        "summary_sent=nlargest(3,sent_score, key=sent_score.get)\n",
+        "\n",
+        "summary_sent"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['In contrast, writing an AI algorithm that covers every eventuality of a task to solve, say, reasoning about data and knowledge to label data automatically (Ratner et al., 2016; Roth, 2017) and, in turn, make, for example, DL less data-hungry–is a lot of manual work, but we know what the algorithm does by design and that it can study and that it can more easily understand the complexity of the problem it solves.',\n",
+              " 'Big Data Is Boosting Intelligent Behavior in Machines\\nMachine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL).',\n",
+              " 'It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”\\n\\nThis is fairly generic and includes multiple tasks such as abstractly reasoning and generalizing about the world, solving puzzles, planning how to achieve goals, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (e.g., creating art or poetry), and controlling robots.']"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "OfROSe94D_-8"
+      },
+      "source": [
+        "# SUMMARY"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/",
+          "height": 191
+        },
+        "id": "ihluYaeqA-5N",
+        "outputId": "a8ec5f9a-9697-4312-ce31-417ad61c8c61"
+      },
+      "source": [
+        "summary=\" \".join(summary_sent)\n",
+        "summary"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'In contrast, writing an AI algorithm that covers every eventuality of a task to solve, say, reasoning about data and knowledge to label data automatically (Ratner et al., 2016; Roth, 2017) and, in turn, make, for example, DL less data-hungry–is a lot of manual work, but we know what the algorithm does by design and that it can study and that it can more easily understand the complexity of the problem it solves. Big Data Is Boosting Intelligent Behavior in Machines\\nMachine learning (ML) and artificial intelligence (AI) are becoming dominant problem-solving techniques in many areas of research and industry, not least because of the recent successes of deep learning (DL). It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”\\n\\nThis is fairly generic and includes multiple tasks such as abstractly reasoning and generalizing about the world, solving puzzles, planning how to achieve goals, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (e.g., creating art or poetry), and controlling robots.'"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From f927bfe9806f079b85d40832f63655d924b541a6 Mon Sep 17 00:00:00 2001
From: AyuavnGautam <91385710+AyuavnGautam@users.noreply.github.com>
Date: Wed, 1 Dec 2021 00:38:43 +0530
Subject: [PATCH 2/2] Update README.md

---
 README.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 54266d1..ec615b4 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,9 @@
-# Probation-Projects-2021
\ No newline at end of file
+# Probation-Projects-2021
+
+Extractive Text Summarization:
+Summarization is a technique to shorten long texts such that the summary has all the important points of the actual document.
+It aims at identifying keywords and salient information that is then extracted and grouped together to form a concise summary, it saves our time and we get a overview
+of the text without losing the overall meaning.
+PYTHON LIBRARIES USED: nltk, matplotlib.pyplot, Wordcloud
+MODULES : heapq , string 
+ Algorithm used for keyword extraction : rake-nltk