TextClassification/chunker.py at master · nonococoleo/TextClassification · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
import nltk
from pickle import dump
from nltk.corpus import conll2000


def tags_since_dt(sentence, i):
    tags = set()
    for word, pos in sentence[:i]:
        if pos == 'DT':
            tags = set()
        else:
            tags.add(pos)
    return '+'.join(sorted(tags))


def npchunk_features(sentence, i, history):
    word, pos = sentence[i]
    if i == 0:
        prevword, prevpos = "<START>", "<START>"
    else:
        prevword, prevpos = sentence[i - 1]
    if i == len(sentence) - 1:
        nextword, nextpos = "<END>", "<END>"
    else:
        nextword, nextpos = sentence[i + 1]
    return {"pos": pos,
            "word": word,
            "prevpos": prevpos,
            "nextpos": nextpos,
            "prevpos+pos": "%s+%s" % (prevpos, pos),
            "pos+nextpos": "%s+%s" % (pos, nextpos),
            "tags-since-dt": tags_since_dt(sentence, i)}


class ConsecutiveNPChunkTagger(nltk.TaggerI):
    def __init__(self, train_sents):
        train_set = []
        for tagged_sent in train_sents:
            untagged_sent = nltk.tag.untag(tagged_sent)
            history = []
            for i, (word, tag) in enumerate(tagged_sent):
                featureset = npchunk_features(untagged_sent, i, history)
                train_set.append((featureset, tag))
                history.append(tag)
        self.classifier = nltk.NaiveBayesClassifier.train(train_set)

    def tag(self, sentence):
        history = []
        for i, word in enumerate(sentence):
            featureset = npchunk_features(sentence, i, history)
            tag = self.classifier.classify(featureset)
            history.append(tag)
        return zip(sentence, history)


class ConsecutiveNPChunker(nltk.ChunkParserI):
    def __init__(self, train_sents):
        tagged_sents = [[((w, t), c) for (w, t, c) in nltk.chunk.tree2conlltags(sent)]
                        for sent in train_sents]
        self.tagger = ConsecutiveNPChunkTagger(tagged_sents)

    def parse(self, sentence):
        tagged_sents = self.tagger.tag(sentence)
        conlltags = [(w, t, c) for ((w, t), c) in tagged_sents]
        return nltk.chunk.conlltags2tree(conlltags)


if __name__ == '__main__':
    test_sents = conll2000.chunked_sents('test.txt')
    train_sents = conll2000.chunked_sents('train.txt')
    train_sents += test_sents
    chunker = ConsecutiveNPChunker(train_sents)
    output = open('chunker.pkl', 'wb')
    dump(chunker, output, -1)
    output.close()

    # print(chunker.evaluate(test_sents))
    # t=nltk.corpus.brown.tagged_sents()[113]
    # print(' '.join(t))
    # print(chunker.parse(t))