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-Ana Tab Window: Passes 22 & 23
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-This section describes the analyzer passes "anaphora" and
- "anaphoraCompany".
-
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-Anaphora Pass
-
-Anaphora is a fancy linguistic word for words and phrases that refer
- to other words and phrases in a text. One common anaphora relates to pronouns
- such as "they" and "its". Other phrases include words
- that reference actions such as "the sale", "this last purchase",
- etc. Below are highlights from the "anaphora" pass:
-
-
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-AnaphoraCompany Pass
-
-First, we simply match an _anaphora node. If we find a suitable company
- candidate in previous sentences (shown further below), then we change
- our _anaphora to be a _company and point to the company found previously
- in the text.
-
-
-
-For debugging purposes, we write the results to a dump file called "anaphora.txt".
- Here is the final output of this pass:
-
-
-
-Searching Through Sentences
-
-Once the rule finds an _anaphora node, we can now use our "parse"
- concept in the KB to search backward for the company being referred to.
- This finally shows the power behind our KB-based analyzer design.
-
-Let us remember where we are. We are matching this rule in the context
- of the @PATH, which is set to match in sentences and therefore we can
- directly access the sentence object we have created under the "parse"
- concept of the KB. In our @CHECK area, we will loop through the previous
- sentences in the KB, and through the objects we created under those sentences,
- in order to find the first object that matches the desired type of anaphora.
-
-For example, if the anaphora is "it", then we will look backward
- for the first "company" type. If the anaphora is an "action"
- type such as "this last purchase", then we will back up to find
- the first "action" type. Although this example is simplified,
- it can be designed to deal with much more complicated and subtle linguistic
- constructions.
-
-We loop through the sentences and through the objects within each sentence.
- We know by design that there will only be two types of objects: "company"
- and "action" so we search for a match accordingly. When we find
- a match, we set S("exit") to 1 and that exits our while loops.
- If we find no object, we call fail ,
- a special function for the @CHECK area that aborts the rule match altogether.
-
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-Next Section:
- Company Conj
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-Ana Tab Window: Pass 24
-
-This section describes the analyzer pass "conjCompany".
-
-
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-ConjCompany Pass
-
-This pass is another example of conjunctions (see "Money"
- section) but this time we are dealing with companies. It is constructed
- in the same manner as the conjMoney pass:
-
-
-
-The conjunction rule was built primarily to handle the following sentence.
- A more general treatment might use a recursive pass to gather conjunctions
- one at a time.
-
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-Next Section:
- Events
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-Ana Tab Window: Passes 19 - 21
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-This section describes the analyzer passes "companies", "companyAttributes",
- and "companyDet".
-
-
-
-Seeing Green
-
-There are currently two colors that appear when highlighting rule matches
- in the input text: blue and green. Usually, the text will highlight in
- blue, representing newly built nodes. When a rule matches without building
- new nodes, the text corresponding to the rule match is highlighted in
- green.
-
-Companies Pass
-
-Below is the highlighting for the company pass in our corporate analyzer,
- pass 19:
-
-
-
-The rule in this pass finds company nodes, but does not alter the parse
- tree. Rather, it updates the KB "parse" area's tracking of company
- instances found in the text, within each sentence of the text. The "parse"
- area was introduced in the initKB pass.
-
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-
-Also, the "sentences" pass creates a numbered sentence concept
- (e.g., "sentence1") for every sentence that it detects in the
- parse tree, as well as having the "_sentence" node in the parse
- tree refer to its corresponding name in the "parse" area. We
- can see this using our handy "View>Tree of Selected" and
- displaying any portion of the text as seen below. The sentence object
- is written as ("name" "sentence1") ("object"
- concept "<name>"):
-
-
-
-With this in mind, we now want to create "company objects"
- and attach them to the sentence object in the "parse" area of
- the KB. Notice below the @PATH command. This directs the rule matcher
- to search for matches only in "_sentence" nodes. In addition,
- we can access the "_sentence" context node from a matched rule
- with the special NLP++ X
- function. X(varname) and X(varname, 3) refer to a variable in the last
- and third component of the @PATH, respectively. Below, we create a new
- company concept with the makeconcept
- function and attach it to the sentence "object". We also add
- the attributes "type" and "normal" to the new company
- concept:
-
-
-
-Green Highlighting
-
-Now that we understand this pass better, let us look at why the company
- rule displays its matches in green.
-
-In the above rule, note two things: the use of _xNIL and the absence
- of tree building functions in the @POST area.
-
-(1) _xNIL is not really a special nonliteral, but it is a convention
- that documents when a rule is intended NOT to create a new, or suggested,
- node.
-
-(2) In NLP++, an empty @POST region uses the single
- reduction by default. But as soon as any code is placed in a @POST region,
- that default is overridden. To get a rule to create a new node, an action
- such as single must be specified
- explicitly in that case.
-
-With a non-empty @POST area that lacks a reduce action, this rule fires,
- but does not change the structure of the parse tree. Because the rule
- fired without modifying the tree, the highlighting appears in green.
-
-CompanyAttributes Pass
-
-Remembering the money pass discussion involving noun phrases, we use
- the same technique to gather attributes in the text surrounding companies.
- Below are the rules that matched company attribute phrases such as "TAI
- (Text Analysis Int'l)", "Founded in 1998, TAI", "California
- company", etc. We leave it to you to investigate the companyAttributes
- pass:
-
-
-
-CompanyDet Pass
-
-The companyDet pass stands for company "determiner" pass.
- Determiner is a linguistic term for those function words we put in front
- of nouns and their adjectives, e.g., "the", "a", "this",
- "that". In keeping with our idea of grabbing the entire company
- phrase, we add this pass to simply attach the determiner to the company
- phrase we are building in our passes 19-21.
-
-One interesting function you may have noticed in the previous pass (pass
- 20) is pncopyvars . This function
- is a handy function that copies all the variables from a matched node
- to the newly created node. In this case, we can think of it as passing
- all the meaning or semantic information from the _company item in the
- rule below, to the new _company tree node being constructed by this rule.
- Also notice the presence of the single
- action. This builds the new node for the rule. We also include the partial
- parse tree for the phrase "the Los Angeles-based AMRP". The
- tree shows the semantic information passed up from the _company node below
- to the _company node above:
-
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-Next Section:
- Anaphora
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-Ana Tab Window: Pass 25
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-This section describes the analyzer pass "events".
-
-
-
-Important:
-
-This is by far the most important section of our corporate analyzer
- tour. Here, everything we have done so far will come together.
-
-Events
-
-In language, it is usual to have some kind of action as the center of
- meaning within a sentence. John kicked
- the ball. The ball was kicked
- by John. Actions are associated with verbs. The corporate world deals
- with sales, mergers, acquisitions, and statements of fact about a company,
- e.g., "Founded in 1972". Below, we can see the events that our
- analyzer recognizes in pass 25:
-
-
-
-Event Rule 1
-
-Our "events" pass 25 contains two rules. One is for a stock
- "event", the second for "buy" and "acquire"
- events. The second rule is :
-
-
-
-There are several remarkable things going on here. First, if you look
- at the graphic above containing the highlighting, you will see that many
- events are highlighted, yet there are only two rules in this pass. The
- second rule is generic enough to match three quite distinct sentences.
-
-This follows from the way in which the analyzer is constructed up to
- the current pass. Remember that we gathered not only company names and
- money values, but also the attributes in the text surrounding these constructs.
- That is, we gathered complex noun phrases to distill the domain-critical
- objects from the text. Once we gather all the subphrases into one neat
- concept such as _company or _money, we can build abstract rules for events,
- as in the second rule above. The idea that "companies buy companies
- for money" is clearly expressed by rules at the event level of abstraction.
-
-In this way, an information extraction system can become a powerful
- tool for accurately and deeply extracting critical content from a text.
-
-Generate Rule
-
-The "Generate Rule" menu item is a super-accelerator for building
- rules by hand. You invoke it from the right-click popup menu of a text
- window. It automatically generates rules from text in much the same way
- that RUG generates rules.
-
-NOTE:
- Generate Rule only works if you have previously run the analyzer on the
- text with the "Generate Logs" toggle turned on:
-
-
-
-Using our other handy function "View>Tree of Selected",
- we can take a look at the tree for pass 25 for sentence 5:
-
-
-
-Examining the top-level nodes of the parse tree above, you will see
- your rule! I.e., "_company have _acquire _company for _money".
- This is what Generate Rule does for you automatically, as follows.
-
-Below, choose the same text and instead of viewing the tree, select
- "Generate Rule". (Note
- that the period is not included in the selected text.) Generate
- Rule goes to the events pass, opens it up, and places a new rule at the
- bottom of the file. You can see the new rule on the left, in the image
- below:
-
-
-
-We can also use Generate Rule to generate another rule for a second
- sentence, with an interesting result in this case:
-
-
-
-Note that the two generated rules differ only by the concept "_have",
- even though the sentences appear to be very different. Also, notice that
- we have left in the "real" rule above the generated rule to
- show you the final form. The final rule shows that "_have" is
- "optional" and that the verb or action for the event is a list
- that matches either _buy or _acquire concepts. In fact, this rule matches
- three sentences that on the surface appear to be very different, but linguistically,
- turn out to be very similar in structure. And the corporate analyzer has
- helped uncover the similarity!
-
-Action Concept
-
-When the event rules fire, we want to create an action object in the
- sentence under the "parse" concept. In the first part of our
- @POST area for the above rule, we add the object in the same way we added
- our object for company in a previous section. The following code in the
- @POST area creates the object in the KB "parse" area:
-
-
-
-You can see the resulting KB structure below after the @POST action
- code has executed:
-
-
-
-Conjunctions
-
-The second part of the @POST area for the second rule is where we recover
- our company and money conjunctions. Again, conjunctions are groupings
- of similar items in a list and are normally tedious to deal with in text
- analysis systems. Remembering that we gathered up _money and _company
- concepts into arrays, we can now easily loop through those arrays to create
- the concepts in our "parse" area of the Knowledge Base. Below,
- you can see the two "while" loops where we loop through the
- arrays for company and money. The functions addstrval
- and addnumval add multiple values
- to a concept's attribute. The first adds a string value and the second
- an integer value.
-
-
-
-One important note here is the use of the special NLP++ function N . Notice
- that in the first conjunction area for companies, we are using N("conj
- count",4) and in the second, for money, we are using N("conj
- count",6). The reason for this is that the _company concept is the
- 4th matching element of the rule, and the _money concept is the 6th. We
- repeat the rule here for convenience:
-
-
-
-Stock Event
-
-The stock event is found as the first rule in the "events"
- pass file and keys off the word "stock" and the verb "to
- be". It works just like the "buy" and "acquire"
- rule described above, but does not process conjunctions. It creates a
- "stock" event in the sentence.
-
-
-
-This rule constructs a slightly different structure in the KB than the
- "buy" or "acquire" event. Whereas the second rule
- constructs an event concept under its sentence, the first rule constructs
- a "stock" concept under the associated company and adds related
- information as attributes under the "stock" concept:
-
-
-
-
-
-
-
-
-Next Section:
- Meta Events
-
-
-
-
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-Ana Tab Window: Passes 5, 6, 7
-
-This section describes the analyzer passes "sentences", "paragraphs",
- and "removeWhite".
-
-
-
-Formatting
-
-It is often useful for an analyzer to track the formatting of the input
- text as part of characterizing it. Pass 5 groups words into sentences.
- It assumes that prior passes, e.g., pass 4 (join), have removed or hidden
- periods (and semicolons, question marks, etc.) that are not likely to
- be end-of-sentence markers in the current text. The resulting parse tree
- is shown below:
-
-
-
-Pass 6 groups the sentences into paragraphs:
-
-
-
-Another convenient step, which helps simplify rules in subsequent passes,
- is to excise white space from the parse tree. In this way, subsequent
- rules need not consider white space. Below is a display of a tree before
- and after spaces are removed. Before:
-
-
-
-The parse tree below results from running Pass 7, called "removeWhite":
-
-
-
-Precision Processing
-
-An advantage of grouping the text into sentences and paragraphs is that
- it makes for a more precise and efficient text analyzer. Subsequent passes
- can be instructed to process only within particular contexts, such as
- sentences or paragraphs. In this way, spurious matches in unintended areas
- of the parse tree are minimized.
-
-The @PATH selector, as shown in the pass file for pass 7 below, instructs
- the rule matcher to start at the root of the parse tree (named "_ROOT"),
- then find children named "_paragraph", then find their children
- named "_sentence". Only when such a "_sentence" node
- is found does the rule matcher attempt to match the rules of the current
- pass.
-
-
-
-
-
-
-
-
-Next Section:
- Rule Generation
-
-
-
-
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-GramTab
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-Ana Tab Window: Passes 8 - 12
-
-This section describes the analyzer passes "city", "company",
- and "state" in the "words" stub region.
-
-
-
-Sample Files
-
-We have included a few automatically generated (or "RUG")
- passes in the corporate analyzer, for the purpose of "dictionary"
- lookup. We describe how we set up this capability in the corporate analyzer.
-
-First, we added a folder called "Samples" under which we have
- created three files: "cities.txt", "companies.txt",
- and "states.txt". Below is "companies.txt". Note that
- each text sample (company name in this case) appears on a separate line.
- Only multi-word phrases are added to the companies.txt file, for reasons
- that will become apparent later.
-
-
-
-Stubs
-
-Next, we created a "stub", or placeholder in the analyzer
- sequence, for automatically generated passes. This allows us to control
- where in the sequence of passes these automatically generated passes will
- occur. Since we are using RUG for a dictionary-like function, the stub
- comes early on in the analyzer flow, right after formatting:
-
-
-
-Gram Tab
-
-The Gram Tab is where we set up a hierarchy of text samples. We created
- a stub concept called "words" and under it, we created rule
- concepts for city, company, and state. The Gram Tab allows us to quickly
- add text samples to the rule concepts, e.g., by importing the sample files
- built above. You can also add samples to the hierarchy manually by highlighting
- a text region and selecting the rule concept in which to place it.
-
-
-
-Setting Auto-Generation Flags
-
-Before using RUG to generate our rules for us, we may need to provide
- constraints and instructions that control the rule-construction process.
- In the case of literal constructs such as company names, we use the Gram
- Concept Properties window to specify that "literal" rules are
- to be generated.
-
-
-
-Invoking RUG
-
-To automatically generate the passes, we now choose "All"
- under "Generate Rules" under the "Analyzer" menu:
-
-
-
-Automatically Generated Rules
-
-Now we can double-click on the rules in the Gram Tab to see the corresponding
- passes that have been built. Note, in pass file 10, the warning about
- editing automatically generated files. Currently, any edits made to such
- a pass file will be overwritten the next time RUG is invoked.
-
-Second, notice the form of the rules. RUG creates rules that match the
- phrase and rewrites them to "_company". The rules also include
- a "@PRE" area that restricts the words to be capitalized or
- not. The form of the rules in this pass file is determined by the flags
- set in the "Properties" window above.
-
-
-
-Highlighting Matches
-
-These pass files are like any other pass files in that you can highlight
- pass matches. In the Gram Tab, you can click on any rule icon and display
- the highlights as show below:
-
-
-
-
-
-
-
-
-Next Section:
- KB & Dictionary
-
-
-
-
-
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-HandDict
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-Ana Tab Window: Passes 13 - 15
-
-This section describes the analyzer passes "lookup", "vocabulary",
- and "phrases".
-
-
-
-Lookup Pass
-
-In the last section, we saw how we can automatically generate a dictionary.
- In this section, we show how to use the Knowledge Base as a dictionary.
- The reason for doing this is that, unlike the current version of the RUG
- passes, we cannot attach additional information to the automatically generated
- rules.
-
-First, look at the @RULES area below. You will notice that we are looking
- for phrases of length one through four. We do this simply by writing 4
- rules, attempting to match the longest phrase first:
-
-
-
-Next, we examine the @CHECK area, which allows us to accept or reject
- a match based on criteria we specify. The objective of this pass is to
- look up the matched phrase in the "companies" area of our KB,
- which we assume was set up before we built our analyzer. To perform the
- lookup, we first fetch the text of the phrase using the phrasetext
- function, assigning the text to a variable called "phrase".
- We search for the phrase within the "companies" KB concept (defined
- in the initKB pass), using the
- findhierconcept function. If no
- such phrase was found in the KB, we reject the rule match with the special
- fail function that exits the current
- rule match.
-
-
-
-In the @POST area, we "normalize" the phrase whose concept
- was found in the KB. Recall that we set up our KB such that we placed
- alternative wordings for our companies under a "normalized"
- form. So, we use the findattr
- function to look for the "synonym" attribute, as above. If it
- is there, we need to set the concept's parent as the normalized form of
- the phrase. We repeat our graphic from KB initialization here for clarification:
-
-
-
-Debug Printouts
-
-To help debug the corporate analyzer, we created a dump file called
- "lookup.txt". The C++ -like "<<" operator conveniently
- pipes values to an output file. Below is a screen shot of the lookup.txt
- file.
-
-
-
-Vocabulary Pass
-
-The "vocabulary" pass illustrates how we can quickly build
- vocabulary lookups by hand.
-
-
-
-Phrases Pass
-
-We can also build a pass for phrases by hand. Note that separating the
- "vocabulary" and "phrases" passes enables rules in
- the later pass to use nonliterals built in the earlier pass. For example,
- the first rule below uses the nonliteral "_conj", or conjunction
- token, to handle things like "CEO and president" or "CEO
- & president".
-
-
-
-
-
-
-
-
-Next Section:
- Money
-
-
-
-
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-Ana Tab Window: Pass 3
-
-This section describes the analyzer pass "initKB".
-
-
-
-Constructing the KB
-
-Because the corporate analyzer is expected to analyze more than one
- text, we need to make sure the "parse" area is cleaned out each
- time. More generally, we need to initialize or reset any parts of the
- KB that the analyzer manages dynamically. If the KB happens to not have
- a "corporate" concept, the analyzer can recover by creating
- that area of the KB from scratch.
-
-
-
-NLP++ Knowledge Base Functions
-
-To initialize the KB, we'll use predefined knowledge base functions
- supplied with NLP++. The initKB pass consists purely of code, with no
- rules, as shown in the screen shot below.
-
-The function findroot returns
- the root node of the KB hierarchy. The first call to findconcept
- looks for a concept named "corporate" directly under the root.
- A global variable named "corporate" is assigned the corporate
- concept, if found (or 0, otherwise). The special function G refers
- to a global variable.
-
-If the corporate concept was not found, then makeconcept
- is used to create it as a child of the root of the hierarchy. (Since the
- corporate concept is absent, that means there is no "companies"
- area with synonyms for company names. Even so, the analyzer can still
- execute, though it may not correlate things like "TAI" and "Text
- Analysis International".)
-
-If the "parse" concept is found in the knowledge base, then
- its descendants are removed with rmchildren .
- This clears the parse area so that the new article can be processed with
- a clean slate.
-
-When initKB is executed, the global variables "corporate",
- "parse", and "companies" point to their respective
- concepts in the KB. A global variable called "section number"
- is also initialized to 0 here.
-
-
-
-
-
-
-
-
-Next Section:
- Join
-
-
-
-
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-
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-Intro
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-Intro
-
-Although VisualText may be the most powerful text processing tool around,
- it may not be obvious to the first time user how to build an analyzer.
- You may know what you want to do, but how do you go about doing it? This
- demonstration analyzer gives a first-time user of VisualText an idea of
- exactly how an analyzer is built.
-
-Notes:
- This demonstration analyzer is meant to stimulate ideas in the VisualText
- user. It does not represent in any way a complete or exemplary example
- of analyzer building. We expect that you've at a minimum studied the Quick Tour and the Tutorials
- in order to follow the Corporate tour.
-
- Now is a good time to open the corporate analyzer, as
- described in the Quick Tour, and examine the input file named "Sold.txt"
- in the Dev folder of the Text Tab.
-
-Our Analyzer
-
-Before we can build an analyzer, we must have a task in mind. In our
- corporate analyzer, we want to pull out information about companies from
- newswires. We can pull out information like "who bought whom for
- how much", "who's stock changed and by how much", and other
- information about companies such as current CEO, founding date, and contact
- information.
-
-Our corporate analyzer will build records that could be used to feed
- a database. This type of analyzer is called an "information extraction"
- system. Here is a peek at the corporate analyzer's first output record
- for our sample text:
-
-
-
-VisualText Features and Tips
-
-Key features that make VisualText powerful, flexible, and easy to use
- and debug are tabulated in the Quick Tour Introduction .
- Some areas we'll focus on in this deeper tour of the Corporate analyzer
- are:
-
-
-
- Multi-Pass Processing
-
- Knowledge Base
-
- Automatic Rule Generation
-
-
-Some useful guidelines gleaned from building information extraction
- systems are:
-
-
-
- Do high confidence work first
-
- Group text into chunks to process
-
- Don't process everything
-
- Don't worry about mistakes if they
- don't hurt you
-
-
-We will see examples of these ideas at work in our corporate analyzer
- example.
-
-Plan of Attack
-
-The next step is to sketch a way to build this analyzer. Building a
- text analyzer is art, not science, and every developer will write a unique
- analyzer.
-
-An initial decision to make is whether your analyzer will gather localized
- information only (i.e., do "shallow" processing), or correlate
- information from various parts of a text (or multiple texts). The deeper
- and more accurate the analyzer, the more correlation you will need to
- do, and the more you will rely on the knowledge base (KB) to store information
- about the domain (e.g., "corporate events") and the current
- text.
-
-The corporate analyzer will utilize the KB in order to demonstrate "going
- deep."
-
-Knowledge Based Analyzers
-
-The first step in building our analyzer is to create a dedicated area
- for it within the KB. We start by making a concept named "corporate" ,
- as below. Under that, we create two concepts: "companies" and
- "parse". The "companies" concept holds the companies
- we are interested in and their alternative wordings, as reference knowledge
- for the analyzer. The "parse" concept stores information that
- the analyzer creates and uses dynamically.
-
-
-
-KB Lookup and Normalization
-
-The knowledge base can support looking up and normalizing words and
- phrases. For example, to store the fact that "Text Analysis International,
- Inc." and "TAI" are synonyms, you can construct a hierarchy
- with "TAI" as parent and place its alternative phrasings below
- it. Under the "companies" concept, we'll place a normalized
- form of a company name, e.g., its abbreviation when it's unique. Under
- the normalized form, we place major textual variants. To explicitly specify
- the relationship between concepts, we add a "synonym" attribute
- to each variant. The screen shot of the KB and Attribute Editor below
- depicts this scheme.
-
-
-
-The "companies" area under our "corporate" concept
- is one we construct beforehand, either by hand or by writing another VisualText
- analyzer to populate the knowledge base for us from a file. The "parse"
- area, on the other hand, is managed dynamically by the corporate analyzer
- while it is processing an input article. We assume, for our demo system,
- that TAI and AMRP are the only companies the analyzer needs to know about!
-
-Parse Concept
-
-In the corporate analyzer, we need to gather information across sentences,
- because information about a given company may be spread throughout the
- article. For instance, "TAI buys AMRP" occurs in the first sentence,
- while the phrase "The sale" in the fourth sentence" refers
- back to the first sentence. If the fourth sentence adds useful information,
- we need to relate the two. Therefore, we use the knowledge base to build
- a model of the article as we process it, enabling correlations across
- sentences. Below is a screen shot of the parse area built by the final
- version of the analyzer for our sample input article.
-
-
-
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-
-
-Next Section:
- Initial KB
-
-
-
-
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-Ana Tab Window: Pass 4
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-This section describes the analyzer pass "Join".
-
-
-
-Tokenization
-
-The tokenize pass, which is
- the first pass of every VisualText analyzer, performs an initial segmentation
- of characters in the input text to alphabetic, numeric, punctuation, and
- white space tokens. However, in practice, we may want to treat constructs
- such as "$9.99" and "No. 1" as tokens. VisualText
- makes it convenient to "join", or "retokenize", a
- phrase as a single token.
-
-The example rule below illustrates how we might handle phrases such
- as "No. 1". The rule itself appears after the @RULES marker.
- It matches a phrases that starts with "No", continues with ".",
- then any amount of white space (denoted by "star"), and finally
- a numeric token. When such a phrase is matched, a new node named "_rank"
- is built to dominate the matched phrase. The @POST action specifies what
- will be done when the rule has been matched. In this case, the suggested
- node, named "_rank", will get a variable named "number"
- with the numeric token's text as its value. That is, "No. 33"
- will lead to creation of a variable named "number" with value
- "33", attached to the "_rank" node. The single
- action specifies that a default reduction of the matched phrase is to
- be executed. The rule below can be used to bleed, or hide, periods that
- we don't want treated as ends of sentences.
-
-
-
-Seeing the Matches
-
-VisualText allows you to quickly see the matches for any given pass.
- The blue text corresponds to nodes built by rule matches in the "join"
- pass.
-
-
-
-Note that the apostrophe-s in possessive constructs is not highlighted
- in blue. That is because the rule that identifies possessives, shown below,
- excises the apostrophe and "s" from the parse tree.
-
-
-
-
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-
-
-
-Next Section:
- Formatting
-
-
-
-
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-Ana Tab Window: Pass 26-28
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-This section describes the three analyzer passes: eventAttributes, anaphoraEvent,
- and events2.
-
-
-
-EventAttributes Pass
-
-Some phrases describe entire events. Below is one of them. The phrase
- "In 2006 alone," describes the "acquisition" event
- that follows. Notice that it is in green. We will explain below.
-
-
-
-This pass takes care of attaching the date to the "acquire"
- event via an attribute. If we look at the resulting information in the
- KB, we can see the final format:
-
-
-
-Notice in the rule itself that the _event item is already matched. This
- pass (26) must come after pass 25 because the rules in pass 26 expect
- that events have already been processed. We create a "date"
- concept below the action concept and add a "year" attribute
- and value to the new date concept. But we do not gather these items under
- a new node. Remember that if we have a @POST area associated with a @RULES
- area, we need to specifically call an NLP++ action in order to "reduce"
- or create a new node under which all the items are gathered. There is
- no "single()" function and the suggested node name is "_xNIL",
- which is a convention telling us that we are not interested in building
- a new node for this rule match. That is why VisualText displays the match
- for this rule in "green " (see text above).
-
-
-
-AnaphoraEvent Pass
-
-Pass 27 is much like pass 23, the anaphoraCompany pass. The purpose
- of this pass is to find all the _anaphora concepts and loop back through
- the sentences under the "parse" area of our KB to find a match.
- Only this time, we are looking for "actions" to match, not "companies".
- Below, you can see that phrases that reference other events in the text
- are highlighted:
-
-
-
-Below you can see the rule and the @POST area for this rule. We are
- matching _anaphora and creating a new parent node _eventAnaphora. Notice
- that since we have a @POST area, we must call the function "single()"
- to create the _eventAnaphora node:
-
-
-
-In the above @POST region, we also print out some information to the
- dump file "anaphoraEvent.txt" for debugging purposes. This way
- we can easily monitor what this pass is choosing as its reference to the
- anaphora:
-
-
-
-Looping Through the Sentences &
- Objects
-
-As in pass 23, we will loop back through the sentence concepts we built
- under "parse" in the KB to find the "action" objects
- we constructed earlier. In layman's terms, we are looking for "action"
- events that match phrases like "The sale", "this last purchase",
- etc. The code below is almost identical with pass 23 with the exception
- of our check for "action" instead of "company":
-
-
-
-Events2 Pass (28)
-
-This pass uses the previous pass in two ways. One, it looks for the
- presence of an _eventAnaphora concept in a particular pattern. Two, once
- it finds it, it makes a "comment" concept under the object that
- _eventAnaphora is pointing to. Remember that in the previous pass (27),
- we looped back through the "parse" area until we found an "action".
- In this case, the phrase "The sale" in the 4th sentence ends
- up attaching a "comment" concept under the "buy" action
- in the second sentence:
-
-
-
-Below is the rule that does this:
-
-
-
-The second rule in pass 28 does something similar:
-
-
-
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-
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-Next Section:
- Output
-
-
-
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-Ana Tab Window: Passes 16 - 18
-
-This section describes the analyzer passes "money", "moneyAttributes",
- and "conjMoney".
-
-
-
-Lessons in Phrases
-
-In diagramming sentences in English class, you may have come across
- noun phrases (NP). These come
- in many many forms and basically represent nouns (persons, places, things)
- and attributes about those nouns (color, amount, type). Knowing this,
- we can write a more organized text analyzer.
-
-In passes 16 - 18, we process money phrases. First we gather the root
- money noun (money), then process its attributes (moneyAttributes), and
- then gather lists of money constructs (conjMoney).
-
-Money
-
-In the money phrase, we key off the word "dollars". Since
- this is a demonstration analyzer, we will only write rules for those patterns
- we are matching in our "Sold.txt" article. The rules below match
- constructs such as "$30 million dollars" and "30 million
- dollars".
-
-
-
-In the @POST area, we perform our "normalization", multiplying
- the numeric value in the text by 1,000,000 to compute the number of dollars.
-
-Money Attributes:
-
-Next, we gather the attributes in the text surrounding the money phrases.
- Study the moneyAttributes pass for yourself. Below is the highlighting
- for this pass. You can easily see the attribute phrases: "more than
- X", "X in cash", "X in assumed debt", "a
- total of X", etc.:
-
-
-
-
-
-Conjunctions
-
-In analyzing text, conjunctions can be very painful to handle. Not so
- in VisualText. NLP++ includes an array construct that can simplify the
- problem of conjoining similar phrases. To illustrate, we will elaborate
- one case.
-
-In the conjMoney pass, we take the money and value and put it in the
- arrays S("conj value") and S("conj type") using the
- counter S("conj count"). These will be used in future passes
- to loop through and recover the money values.
-
-
-
-Tree of Selected
-
-Below is the matched conjunction phrase and the tree for that phrase.
- Two useful features of VisualText should be pointed out here. One is that
- the semantic information is displayed in the trees. Below, you can see
- the result of firing the above conjunction rule: ("conj count"
- 2) ("conj value" 120000000140000000) ("conj type"
- "cash" "assumed debt").
-
-The second convenient function is found in the right-click menu of the
- processed text window. Select the pass you are interested in (usually
- it is already selected), then select any portion of text you want. Using
- the right-click menu, choose "View>Tree of Selected" and
- the partial parse tree for that text will appear in the context of the
- chosen pass. This is a handy function to remember.
-
-
-
-
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-
-
-Next Section:
- Companies
-
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-
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-
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-Output
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-Ana Tab Window: Pass 29
-
-This section describes the last analyzer pass, "output". (The
- complete Corporate Analyzer sequence is shown below.)
-
-
-
-Output
-
-One of the beauties of using a KB-based text analyzer is that once you
- have everything processed, the output is a simple matter of looping through
- the "sentence" concepts in the "parse" area in the
- KB and printing out the results. If we open up the sentences under the
- "parse" concept, we can easily see what we want to output to
- the file. The rule is, if there is a child concept of the object under
- the sentences, print it out. If not, don't output anything. Below, this
- is clearly illustrated by looking at the concept for "TAI".
- Those with a "+" have something to output, those without do
- not:
-
-
-
-We leave it to you to look at the details of the code for this pass.
- Please note, however, that there is no rule for this pass, just a @CODE
- area. The last rule-matching pass was pass 28, and all that remains is
- to output our results. Notice, when inspecting the output file, that information
- from later sentences has been copied back to earlier sentences. This is
- due to our analyzer's treatment of "anaphora". Namely, information
- later in the text has been correlated to references earlier in the text.
- Below is one of the more interesting outputs, taken from sentence 5:
-
-
-
-
-
-
-
-
-Go forth and build better
- analyzers!
-
-
-
-
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-TOC
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-CORPORATE ANALYZER
-Table of Contents
-
-
-
-
-
-
-
-Pass #
-
-Name
-
-Description
-
-
-
-
-
-Intro
-
-Purpose and strategy for building the corporate analyzer
-
-
-
-3
-
-Initial KB
-
-Knowledge Base initialization
-(Pass: InitKB)
-
-
-
-4
-
-Join
-
-Tokenization and retokenization
-(Pass: Join)
-
-
-
-5-7
-
-Formatting
-
-Formatting text into chunks
-(Passes: Sentences, Paragraphs, RemoveWhite)
-
-
-
-8-12
-
-Rule Generation
-
-Working with automatic rule generation in the Gram Tab
-(Passes: City, Company, State)
-
-
-
-13-15
-
-KB & Dictionary
-
-Dictionaries built by hand
-(Passes: Lookup, Vocabulary, Phrases)
-
-
-
-16-18
-
-Money
-
-Money phrases and lists
-(Passes: Money, MoneyAttributes, ConjMoney)
-
-
-
-19-21
-
-Companies
-
-Company phrases
-(Passes: Companies, CompanyAttributes, CompanyDet)
-
-
-
-22-23
-
-Anaphora
-
-Words that reference previous locations in a text
-(Passes: Anaphora, AnaphoraCompany)
-
-
-
-24
-
-Company Conj
-
-List of companies in text
-(Pass: ConjCompany)
-
-
-
-25
-
-Events
-
-Buy, Acquire, and Stock events
-(Pass: Events)
-
-
-
-26-28
-
-Meta Events
-
-Event attributes and phrases that refer back to other
- events
-(Passes: EventAttributes, AnaphoraEvent, Events2)
-
-
-
-29
-
-Output
-
-Outputting the data to a file
-(Pass: Output)
-
-
-
-
-
-
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-Tour AnalyzerTab
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-
-
-The Analyzer
-
- Click on the Ana Tab button to display the Ana Tab Window.
-
-This window maintains the ordered sequence of passes that define the
- current text analyzer. System or built-in pass types (e.g., tokenize)
- are represented by flat "DNA" icons. Passes with associated
- pass files have twisted DNA icons. Our corporate analyzer has 29 passes:
-
-
-
-Stubs
-
-You may have noticed that there are two passes with red circles. These
- represent "stubs" or placeholders in which automatically generated
- passes are deposited. We'll talk about automatic rule generation and the
- Gram Tab in a later section:
-
-Double Click
-
-The most common action in the Ana Tab is to open up a pass file by double-clicking
- on the desired pass:
-
-
-
-Right-Click and Toolbar
-
-The Ana tab has an extensive right-click menu as well as arrows in the
- Tab Toolbar that serve for deleting or moving analyzer passes:
-
-
-
-
-
-
-
-
-
-
-
-
-Next Section:
- Pass Files
-
-
-
-
-
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-Tour Debug
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-Examining the Analyzer Under Construction
-
-There are numbers of tools and methods for debugging analyzers, including
- highlighting and what is called "Page Mode".
-
-Highlighting
-
- Highlighting is a simple and intuitive tool for debugging
- analyzers. Turn on the highlighting toggle in the toolbar:
-
-
-
- Then open up Sold.txt and select the Ana Tab window.
- Single click on various analyzer passes. The matched text for each pass
- will be highlighted in blue and green, letting you know which words and
- phrases matched in that pass:
-
-
-
-Page Mode
-
-Another important debugging tool is called "Page Mode":
-
-
-
-Among other things, it allows you to examine the change in parse trees
- for each pass.
-
- With the "Page Mode" on, choose the phrase
- "$120 million in cash and $14.3 million in assumed debt" and
- view its tree. Then in the Ana Tab, click on pass 4, "join",
- and travel down the analyzer passes observing the changes in the parse
- tree:
-
-
-
-
-
-
-
-
-Next Section:
- KB
-
-
-
-
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-Tour Find
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-
-
-Find Window
-
-One other important window in the VisualText interface is the Find window.
- You can search for a word or phrase in specified files and in other analyzers.
- Below, we search for the concept "_company" using Find in Files
- :
-
-
-
-Double-Click
-
-Once you have searched on a word, the find window allows you to go directly
- to an instance of the word or phrase by double-clicking on the line of
- interest:
-
-
-
-This will take you directly to the window and highlight the instance:
-
-
-
-
-
-
-
-
-Hope you enjoyed the
- tour of VisualText!
-
-
-
-
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-Tour GramTab
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-Automatic Rule Generation (RUG)
-
-In VisualText, rules can be automatically generated from text samples.
- In our corporate analyzer, we use the Gram Tab much like a "dictionary".
- More complex samples with subsamples can also be managed in the Gram Tab.
-
-
-
-Stubs
-
-Stubs are place holders where the automatically generated rules will
- appear in the Analyzer Sequence. Notice that the icon for words above
- matches the "stub_words" pass below, taken from the Analyzer
- Tab:
-
-
-
-Double-Click
-
-If you double-click on a sample such as "New Hampshire Medical",
- the text file of the sample will pop up in the Workspace. In our corporate
- analyzer, we are using sample files for our dictionaries. One can imagine
- using large lists of words and phrases in files that can be converted
- into rules automatically:
-
-
-
-If you double click on a rule concept (one with a right-pointing arrow)
- such as "company", you can see its corresponding pass file containing
- automatically generated rules:
-
-
-
-
-
-
-
-
-Next Section:
- Debugging
-
-
-
-
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-Tour Intro
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-
-
-VisualText Features and Benefits
-
-VisualText™ is an Integrated Development Environment for building deep
- text analysis applications. It is like Visual C++(R), but with specializations
- for Natural Language Processing. Some features and benefits of VisualText:
-
-
-
-
-
-
-FEATURE
-
-BENEFIT
-
-
-
-Integrated development environment (IDE)
-
-Reduces resources needed to build text analyzers
-
-
-
-Rich, integrated GUI tool set and data views
-
-Accelerates development of deep, accurate
- analyzers
-
-
-
-NLP++ Programming Language
-
-Anything thinkable can be programmed! Users
- focus on the task and heuristics rather than programmatic detail
-
-
-
-Hierarchical Knowledge
- Base Management System
-
-Easy to build ontologies, dictionaries, semantics,
- and any other desired schemas.
-
-
-
-Automatic Rule Generation (RUG)
-
-Easy and quick to maintain and enhance analyzers
- merely by highlighting text.
-
-
-
-NLP++ integrates code, rules, parse trees,
- and KB
-
-Easy to access/modify knowledge.
-
-
-
-Interpreted NLP++ execution
-
-Blazing fast modify-and-test development cycle
-
-
-
-Compilation of analyzer and KB
-
-Fast execution in runtime environment
-
-
-
-Excellent GUI support for debugging analyzers
-
-Reduced time and effort to debug analyzers
-
-
-
-Multi-pass analyzers
-
-Modular, maintainable, readable, extensible
-
-
-
-Single parse tree
-
-No combinatorial explosion; results in efficient
- analyzers.
-
-
-
-Paradigm independent, integrates multiple
- paradigms and styles of text analysis
-
-Enables developer to use and combine the most
- appropriate methods for the task at hand
-
-
-
-User project for adding C++ code
-
-Open architecture for easy integration with
- 3rd party software, extension of the core capability by the customer.
-
-
-
-Rules operate in specific contexts
-
-Higher precision, faster execution.
-
-
-
-Built-in confidence operator
-
-Easy to compute and assess confidence
-
-
-
-
-
-
-
-
-Sample Applications
-
-
-
- Information
- Extraction - Systems that accurately extract, correlate, and standardize
- the important content of text.
-
- Shallow Extraction - Systems that accurately identify names, locations, dates,
- and other atomic features of text.
-
- Search
- - Quality search capabilities on the World Wide
- Web and other electronic text sources.
-
- Filtering - Systems that are both accurate and fast, to determine if
- a document is relevant.
-
- Categorization - Systems to determine the topic of documents.
-
- Data Mining - Finding
- important nuggets of information in voluminous texts.
-
- Test Grading - Reading
- and matching prose tests with idealized answers.
-
- Summarization - Building a brief, accurate description of the contents of
- a text.
-
- Database
- Entry - E.g., resumé processing, medical reports, and police reports.
-
- Natural Language
- Query - The ability to ask a computer questions using plain text.
-
- Dissemination - Routing
- documents to people or locations that require them .
-
-
-GUI
-
-VisualText's comprehensive interface allows you to focus on the main
- goal: quickly building an accurate and complete text analyzer.
-
-
-
-NLP++ Language
-
-NLP++™ is a new C++ -like programming language specially designed for
- building deep text analyzers:
-
-
-
-Knowledge Base
-
-VisualText integrates a powerful and flexible Knowledge Base that allows
- analyzers to be as smart as you want them to be:
-
-
-
-Debugging Tools
-
-A suite of debugging tools lets you highlight rule matches, examine
- parse trees, and study your analyzer one step at a time:
-
-
-
-Flexible, Easy to Maintain, and Fast
-
-VisualText is not tied to one style or paradigm of text analysis. If
- you can imagine a text analyzer, then VisualText can build it!
-
-Being "visual" means easy to build and maintain. VisualText
- displays text analyzers in such a way as to make it easy for anyone to
- modify or extend existing analyzers.
-
-And finally, VisualText is fast. It can substantially cut the cost and
- time needed to build text analyzers. Text analyzers under development
- are run in interpreted mode, so that you can quickly modify and test your
- analyzer. Once a text analyzer is built, you can compile it to run faster.
-
-
-
-
-
-
-Next Section:
- Screen Layout
-
-
-
-
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-
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-Tour KB
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-
-Knowledge Base
-
-A Knowledge Base or "KB" is like a database but differs in
- several respects: it is hierarchical and is managed dynamically
- by the analyzer. Databases are designed first, then used by programs.
- Knowledge Bases are used dynamically with text analyzers and change as
- the analyzer processes a text.
-
- Open the KB by selecting the KB Editor toolbar button.
- Open concepts by clicking the + to their left:
-
-
-
-Double-Click
-
-The corporate analyzer makes heavy use of the KB. Above, there are two
- areas under the "corporate" concept: "companies",
- and "parse". The companies area is static and is used in looking
- up synonyms of company names. The "parse" area is used by the
- analyzer to reconstruct the "corporate" world by creating concepts
- under each sentence concept, in tandem with processing an input text.
-
-Below, we show the result of double-clicking on the "acquire"
- concept under sentence 5, then double-clicking on the "comment"
- concept. Note that the analyzer has created attributes that describe the
- "acquire" event:
-
-
-
-
-
-
-
-
-Next Section:
- Find
-
-
-
-
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-Tour Load
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-
-
-
-
-
-
-Open the "Corporate" Analyzer
-
- (Actions for you to take are flagged by a yellow arrow
- at the start of a line.) Now it's time for you to load the Corporate Analyzer,
- which can be done in two ways. The first starts with double-clicking the
- desktop icon for VisualText:
-
-
-
-The second is by double-clicking the icon
- for a particular VisualText analyzer. The icon for the Corporate analyzer
- looks like:
-
-
-
-Method One: Loading an analyzer within
- VisualText
-
- Open VisualText. Next, choose "Open Analyzer"
- under the main menu "File":
-
-
-
- You are then prompted to choose an analyzer. Browse to
- c:\Program Files\TextAI\VisualText\apps\corporate and open the file named
- corporate.ana (it may be listed as corporate):
-
-
-
-Method Two
-
- Locate the corporate analyzer in your Windows File Explorer
- window. Then double-click on the VisualText analyzer file (.ana). This
- will invoke VisualText and load the corporate analyzer:
-
-
-
-You are Now Ready to Start!
-
-Once you have VisualText and the Corporate Analyzer loaded, we can continue
- our tour.
-
-
-
-
-
-
-Next Section:
- Log Window
-
-
-
-
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-Tour Log
-
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-
-
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-
-
-
-
-
-
-Log Window
-
-In the bottom left-hand corner resides the Log Window, which monitors
- the status of the currently loaded analyzer. Analyzer load and run errors
- are shown in the Log Window.
-
-
-
-
-
-
-
-
-Next Section:
- Text Tab
-
-
-
-
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-Tour Output
-
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-
-
-
-
-Examining the Output
-
- After running the corporate analyzer on the input text,
- you can now examine the output in either of two ways. One is to click
- on the "Output" button in the Debug Toolbar:
-
-
-
-The second is to click the button next to the Output button and choose
- the "output" file from the available dump files:
-
-
-
-The Output File
-
-The output below shows the purpose of the Corporate analyzer, which
- is to extract and correlate key business information from the Sold.txt
- file. The name "output.txt" is a special file name in VisualText,
- that has its own button on the toolbar. If the analyzer does not output
- to a file called "output.txt", the button will be greyed out.
- Below is a portion of the output file from our Corporate analyzer. Take
- some time to examine the entire content of the output file in VisualText.
-
-
-
-
-
-
-
-
-Next Section:
- Parse Tree
-
-
-
-
-
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-Tour ParseTrees
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-
-Parse Trees
-
-VisualText builds multiple pass analyzers in which each pass uses the
- parse tree as modified by the
- previous pass. A parse tree is a data structure that records and groups
- patterns discovered in the input text. The elements of a parse tree are
- called nodes. Each line in the parse tree display below represents one
- node, or phrase, within the parse tree. Nodes may carry information in
- the form of node variables and values.
-
-Tree Buttons
-
- Select the "events2" pass in the Ana Tab, then
- click the "View Parse Tree" button in the Debug Toolbar, as
- shown below.
-
-In this way, you can view the parse tree for the pass that is currently
- selected in the Ana Tab. (Note: If the text has been analyzed with Generate
- Logs off, then the final parse tree is displayed.) The display shows patterns
- and groupings identified in the input text, as well as the node variables
- and their values as parenthesized lists of the form ("variable_name"
- "value1" "value2" ...).
-
-
-
-Viewing Partial Trees:
-
-VisualText also allows users to display partial parse trees for selected
- parts of the text. The phrase "$14.3 million in assumed debt"
- is highlighted in the text window and displayed in a parse tree window,
- below.
-
-
-
-
-
-
-
-
-Next Section:
- Ana Tab
-
-
-
-
-
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-Tour PassFiles
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-
-
-
-Heart of the Analyzer
-
-The heart of the text analyzer lies in the pass files, which contain
- NLP++ code and rules.
-
-Pass File Zones, Regions, and Selectors
-
-Pass file areas are marked by words that are blue, all-caps, and start
- with an "@". Three commonly used regions are detailed below.
-
-
-
-
-
-
-AREA
-
-DESCRIPTION
-
-EXAMPLE BELOW
-
-
-
-@PATH
-
-A Selector that defines the context in which
- rules will be tried.
-
-In the corporate analyzer, most paths are
- defined as applying to sentences under paragraphs,under the root node
- of the parse tree.
-
-
-
-@RULES
-
-Marks the start of a Rule Region. An NLP++
- rule consists of a pattern (called a phrase) that is matched against the
- parse tree.
-
-Below, the rules match patterns for constructs such as $120 million
- or 160 million dollars.
-NOTE: words with underscores (_money) are nonliterals, or abstract
- tokens, while those without match raw text ("dollars");
-
-
-
-@POST
-
-When a rule matches, NLP++ code in the Post
- Region tells the rule matcher what actions to take. See the _money rules
- below.
-
-Below, the numeric value for the money phrase
- is calculated and stored on the suggested node.
-
-
-
-
-
-
-Code Region
-
-An NLP++ pass file includes a CODE Region that is independent of any
- rule matching. One example of this is in the third pass "initKB",
- where the analyzer constructs needed concepts in the Knowledge Base before
- processing the text:
-
-
-
-
-
-
-
-
-Next Section:
- Gram Tab
-
-
-
-
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-
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-Tour Run
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-
-
-
-
-
-Running the Analyzer on Text
-
- First, select the text you want to process, in the Text
- Tab:
-
-
-
- To view the text, either double-click on the text icon
- (above) in the Text Tab, or go to the Debug Toolbar and click on the "paper"
- button:
-
-
-
-The text window pops up in the workspace:
-
-
-
-The Run Button
-
- To run the analyzer on the selected input text, choose
- the running-man button in the Workspace Toolbar:
-
-
-
-The cursor becomes an hour glass and the stop button is activated:
-
-
-
-The Log Window will display stats about the run, including any syntax
- errors it discovered:
-
-
-
-You have successfully run the corporate analyzer on our "Sold.txt".
-
-
-
-
-
-
-Next Section:
- Output
-
-
-
-
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diff --git a/visualtext/Help/helps/HowToBuild/Tour15/Screen/Tour_Screen.htm b/visualtext/Help/helps/HowToBuild/Tour15/Screen/Tour_Screen.htm
deleted file mode 100644
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-
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-
-Tour Screen
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-
-Screen Layout
-
-The general screen layout of VisualText includes Toolbars, the Main
- Menu, the Tab Window, the Workspace, and the Log and Find Windows:
-
-
-
-The principal controls are the Main Menu, the Toolbars, and the Tabs.
-
-Main Menu
-
-The main menu is a superset of the buttons available in the Toolbars:
-
-
-
-Toolbars
-
-VisualText has 5 toolbars: Main, Browser, Tab, Knowledge Base, and Debug:
-
-
-
-The table below describes each Toolbar:
-
-
-
-
-
-
-
-TOOLBAR
-
-DESCRIPTION
-
-
-
-Main
-
-Standard functions found in all GUI applications,
- including open, close, cut, and paste
-
-
-
-Browser
-
-Functions for managing web browser windows
-
-
-
-Tab
-
-Functions for managing the Gram, Ana, and
- Text Tabs
-
-
-
-Workspace
-
-Functions for the Analyzer and Knowledge Base
-
-
-
-Debug
-
-Tools for examining the input text file, trees,
- pass file, etc.
-
-
-
-
-Tab Window
-
-The Tab Window area accesses three key windows: the Gram Tab, Ana Tab,
- and Text Tab.
-
-
-
-The table below describes each:
-
-
-
-
-
-
-
-TAB
- WINDOW
-
-DESCRIPTION
-
-
-
-Gram
-
-Manage user-added text samples for automatic
- rule generation
-
-
-
-Ana
-
-Manage the analyzer sequence and pass files
-
-
-
-Text
-
-Manage input files, output files, and other
- folders and files
-
-
-Right Click Menus
-
-Right-click popup menus play an important role in VisualText. The Gram
- Tab, Ana Tab, Text Tab, Knowledge Base, Pass File Windows, Parse Tree
- Windows, Text Windows all employ right-click menus to provide context-sensitive
- functions quickly. Below is the right-click menu for the Text Tab:
-
-
-
-
-
-
-
-
-Next Section:
- Loading
-
-
-
-
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diff --git a/visualtext/Help/helps/HowToBuild/Tour15/TextTab/Tour_TextTab.htm b/visualtext/Help/helps/HowToBuild/Tour15/TextTab/Tour_TextTab.htm
deleted file mode 100644
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-
-
-
-
-
-
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-
-
-
-Tour TextTab
-
-
-
-
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-
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-
-
-
-
-
-
-
-
-
-Choosing the Text to Process
-
- Click the Text Tab and open the Dev folder within there.
-
- If Sold.txt has no red checkmark, then click Generate
- Logs in the Analyzer menu and select Run from the Analyzer menu.
-
-The Text Tab is a repository for texts to be processed by your analyzer.
- You can organize text files in folders and run the analyzer on individual
- files or on entire subtrees of folders and files. Inside the "Dev"
- folder, we've selected the text file "Sold.txt" for processing:
-
-
-
-Choosing Previously Processed Text
- Files
-
-In the Tab Toolbar,a recent-text pulldown provides quick access to the
- most recently selected texts. This is useful when the Text Tab holds many
- files:
-
-
-
-Processed Text
-
-When a text has been processed, a "+" will appear in front
- of the icon and the icon will have a checkmark. You can open the text
- file icon and see the log files produced during the analysis (or "run")
- of the text:
-
-
-
-
-
-
-
-
-Next Section:
- Running
-
-
-
-
diff --git a/visualtext/Help/helps/HowToBuild/Tour15/Toc/Tour_TOC.htm b/visualtext/Help/helps/HowToBuild/Tour15/Toc/Tour_TOC.htm
deleted file mode 100644
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-
-
-
-
-
-
-
-
-
-
-Tour TOC
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
-
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-
-
-
-
-
-
-The VisualText Quick Tour
-Table of Contents
-
-
-
-
-
-
-
-Title
-
-Description
-
-
-
-Intro
-
-Introduction to VisualText's Power
-
-
-
-Screen
- Layout
-
-General layout of the Graphic User Interface
-
-
-
-Loading Analyzer
-
-Load the demonstration analyzer "Corporate"
-
-
-
-Log
-
-Monitoring the Log File
-
-
-
-Text
- Tab
-
-Text for developing text analyzers
-
-
-
-Running the
- Analyzer
-
-Run the analyzer on the text
-
-
-
-Output
-
-Examining the output
-
-
-
-Parse
- Trees
-
-Structures built by the analyzer
-
-
-
-Ana
- Tab
-
-Building the analyzer sequence
-
-
-
-Pass
- Files
-
-Rules and Code in pass files
-
-
-
-Gram
- Tab
-
-Automatic Rule Generation
-
-
-
-Debugging
-
-Examining the details of the analyzer at work
-
-
-
-Knowledge Base
-
-Exploring the Knowledge Base
-
-
-
-Find
-
-Finding words and concepts in files
-
-
-
-
-
-
diff --git a/visualtext/Help/helps/Introduction/VisualText_Quick_Tour.htm b/visualtext/Help/helps/Introduction/VisualText_Quick_Tour.htm
deleted file mode 100644
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@@ -1,84 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-VisualText Quick Tour
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-VisualText Quick Tour
-
-Welcome to the VisualText™ Quick Tour, which introduces the major components
- of VisualText and shows how these components work together to create,
- run, and debug a text analyzer.
-
-The Quick Tour walks you through a working analyzer called the Corporate
- Analyzer. The Corporate Analyzer demonstrates the features of VisualText
- and introduces you to building analyzers using VisualText. It is intended
- as a demonstration analyzer only and is meant to be used with the sample
- input files provided.
-
-In addition to the Quick Tour, we have provided a more detailed tour
- of the Corporate Analyzer in the Sample Analyzers "section" (or
- "folder" in the Help). The full tour takes you through the step-by-step
- process used in building the Corporate Analyzer.
-
-We recommend that you run the Corporate Analyzer application as you
- go through both the Quick Tour and the more detailed Corporate Analyzer
- tour. The Corporate Analyzer application is located in the APPS folder
- under your VisualText installation directory, in a folder named corporate .
-
-To run the analyzer, follow the steps outlined below:
-
-
-
- Launch VisualText .
-
- From the File
- menu, select Open Analyzer .
-
- In the Open
- dialog, navigate to the top level corporate folder (c:\Program Files\TextAI\VisualText\apps\corporate,
- by default.)
-
- Select the corporate.ana
- file and click Open .
-
-
-Start
- the Quick Tour.
-
-
-
-
diff --git a/visualtext/Help/helps/SPLASH_SCREEN/Welcome.htm b/visualtext/Help/helps/SPLASH_SCREEN/Welcome.htm
index b118a738..ba79f0b1 100644
--- a/visualtext/Help/helps/SPLASH_SCREEN/Welcome.htm
+++ b/visualtext/Help/helps/SPLASH_SCREEN/Welcome.htm
@@ -68,9 +68,7 @@
margin-right: 0px;"
width=19
height=16
- border=0> *** New to VisualText? Start by taking the Quick Tour *** New to VisualText? Start with the NLP++ Textbook ! ***
-
-
-
-
-
-
-
-
-
-Corporate Analyzer
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-Corporate Analyzer Tour
-
-The tour of the Corporate Analyzer (in the VisualText\apps folder) is
- intended to take you through the step by step process used in building
- the Corporate Analyzer. It will give you an idea of the approach and techniques
- used to build analyzers. This tour is a supplement to the VisualText Quick
- Tour. Study
- of the tutorials is also recommended for a more complete understanding
- of this tour.
-
-We recommend you run the Corporate Analyzer application as you go through
- the tour. This will give you an opportunity to see how the analyzer works
- as you read through the discussion.
-
-Tour the Corporate Analyzer.
-
-
-
-
-
-
diff --git a/visualtext/Help/helps/Using_VisualText/Working_with_Debugging_Tools/DumpFiles.htm b/visualtext/Help/helps/Using_VisualText/Working_with_Debugging_Tools/DumpFiles.htm
index 75d219c7..cb448d1e 100644
--- a/visualtext/Help/helps/Using_VisualText/Working_with_Debugging_Tools/DumpFiles.htm
+++ b/visualtext/Help/helps/Using_VisualText/Working_with_Debugging_Tools/DumpFiles.htm
@@ -76,7 +76,7 @@
In the pass file, add a printing statement using
the "<<" operator in the following form: "dump_file_name.txt"
<< content_to_print. Below
- is a rule from the corporate analyzer that dumps the matched rule text
+ is a rule from an early sample analyzer that dumps the matched rule text
into a dump file called "anaphora":
diff --git a/visualtext/Help/helps/VisualText_Basics/DebuggingOverview.htm b/visualtext/Help/helps/VisualText_Basics/DebuggingOverview.htm
index 2903acaa..2d323b8e 100644
--- a/visualtext/Help/helps/VisualText_Basics/DebuggingOverview.htm
+++ b/visualtext/Help/helps/VisualText_Basics/DebuggingOverview.htm
@@ -57,8 +57,8 @@
Working with Debugging Tools
VisualText provides a number of debugging tools to make building text
- analyzers easier. Screen shots from this section are from the VisualText
- Sample "Corporate" analyzer.
+ analyzers easier. Screen shots from this section are from an early VisualText
+ sample analyzer.
Debug Structure:
@@ -232,7 +232,7 @@ Dump Files
One convenient Right-Click menu function will get you started in printing
to a dump file. It printouts out a line to a "txt" file with
the same name as the pass file. In this case, we clicked on the "lookup"
- pass in our corporate analyzer and the Right-Click menu adds the line
+ pass in a sample analyzer and the Right-Click menu adds the line
above the menu as shown below:
Writing Analyzers with VisualText
The VisualText Basics section includes a set of topics designed to help
you build analyzers. As a prerequisite, we recommend you work through
- the Tutorials to understand the workings of the VisualText GUI. Also,
- working through the Corporate analyzer documentation and studying the
- Corporate analyzer will provide a feel for the ordering and specification
+ the Tutorials to understand the workings of the VisualText GUI. Studying complete analyzers, such as those in the NLP++ Textbook,
+ will provide a feel for the ordering and specification
of the steps, or passes, in a text analyzer.
Building text analyzers is more art than science. VisualText favors