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181 lines
6.1 KiB
Python
181 lines
6.1 KiB
Python
# Natural Language Toolkit: RTE Classifier
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Ewan Klein <ewan@inf.ed.ac.uk>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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Simple classifier for RTE corpus.
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It calculates the overlap in words and named entities between text and
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hypothesis, and also whether there are words / named entities in the
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hypothesis which fail to occur in the text, since this is an indicator that
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the hypothesis is more informative than (i.e not entailed by) the text.
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TO DO: better Named Entity classification
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TO DO: add lemmatization
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"""
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from __future__ import print_function
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from nltk.tokenize import RegexpTokenizer
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from nltk.classify.util import accuracy, check_megam_config
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from nltk.classify.maxent import MaxentClassifier
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class RTEFeatureExtractor(object):
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"""
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This builds a bag of words for both the text and the hypothesis after
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throwing away some stopwords, then calculates overlap and difference.
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"""
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def __init__(self, rtepair, stop=True, use_lemmatize=False):
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"""
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:param rtepair: a ``RTEPair`` from which features should be extracted
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:param stop: if ``True``, stopwords are thrown away.
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:type stop: bool
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"""
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self.stop = stop
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self.stopwords = set(
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[
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'a',
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'the',
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'it',
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'they',
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'of',
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'in',
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'to',
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'is',
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'have',
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'are',
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'were',
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'and',
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'very',
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'.',
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',',
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]
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)
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self.negwords = set(['no', 'not', 'never', 'failed', 'rejected', 'denied'])
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# Try to tokenize so that abbreviations, monetary amounts, email
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# addresses, URLs are single tokens.
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tokenizer = RegexpTokenizer('[\w.@:/]+|\w+|\$[\d.]+')
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# Get the set of word types for text and hypothesis
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self.text_tokens = tokenizer.tokenize(rtepair.text)
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self.hyp_tokens = tokenizer.tokenize(rtepair.hyp)
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self.text_words = set(self.text_tokens)
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self.hyp_words = set(self.hyp_tokens)
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if use_lemmatize:
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self.text_words = set(self._lemmatize(token) for token in self.text_tokens)
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self.hyp_words = set(self._lemmatize(token) for token in self.hyp_tokens)
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if self.stop:
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self.text_words = self.text_words - self.stopwords
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self.hyp_words = self.hyp_words - self.stopwords
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self._overlap = self.hyp_words & self.text_words
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self._hyp_extra = self.hyp_words - self.text_words
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self._txt_extra = self.text_words - self.hyp_words
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def overlap(self, toktype, debug=False):
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"""
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Compute the overlap between text and hypothesis.
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:param toktype: distinguish Named Entities from ordinary words
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:type toktype: 'ne' or 'word'
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"""
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ne_overlap = set(token for token in self._overlap if self._ne(token))
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if toktype == 'ne':
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if debug:
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print("ne overlap", ne_overlap)
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return ne_overlap
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elif toktype == 'word':
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if debug:
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print("word overlap", self._overlap - ne_overlap)
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return self._overlap - ne_overlap
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else:
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raise ValueError("Type not recognized:'%s'" % toktype)
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def hyp_extra(self, toktype, debug=True):
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"""
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Compute the extraneous material in the hypothesis.
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:param toktype: distinguish Named Entities from ordinary words
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:type toktype: 'ne' or 'word'
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"""
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ne_extra = set(token for token in self._hyp_extra if self._ne(token))
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if toktype == 'ne':
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return ne_extra
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elif toktype == 'word':
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return self._hyp_extra - ne_extra
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else:
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raise ValueError("Type not recognized: '%s'" % toktype)
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@staticmethod
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def _ne(token):
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"""
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This just assumes that words in all caps or titles are
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named entities.
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:type token: str
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"""
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if token.istitle() or token.isupper():
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return True
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return False
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@staticmethod
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def _lemmatize(word):
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"""
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Use morphy from WordNet to find the base form of verbs.
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"""
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lemma = nltk.corpus.wordnet.morphy(word, pos=nltk.corpus.wordnet.VERB)
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if lemma is not None:
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return lemma
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return word
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def rte_features(rtepair):
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extractor = RTEFeatureExtractor(rtepair)
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features = {}
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features['alwayson'] = True
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features['word_overlap'] = len(extractor.overlap('word'))
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features['word_hyp_extra'] = len(extractor.hyp_extra('word'))
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features['ne_overlap'] = len(extractor.overlap('ne'))
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features['ne_hyp_extra'] = len(extractor.hyp_extra('ne'))
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features['neg_txt'] = len(extractor.negwords & extractor.text_words)
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features['neg_hyp'] = len(extractor.negwords & extractor.hyp_words)
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return features
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def rte_featurize(rte_pairs):
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return [(rte_features(pair), pair.value) for pair in rte_pairs]
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def rte_classifier(algorithm):
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from nltk.corpus import rte as rte_corpus
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train_set = rte_corpus.pairs(['rte1_dev.xml', 'rte2_dev.xml', 'rte3_dev.xml'])
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test_set = rte_corpus.pairs(['rte1_test.xml', 'rte2_test.xml', 'rte3_test.xml'])
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featurized_train_set = rte_featurize(train_set)
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featurized_test_set = rte_featurize(test_set)
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# Train the classifier
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print('Training classifier...')
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if algorithm in ['megam', 'BFGS']: # MEGAM based algorithms.
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# Ensure that MEGAM is configured first.
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check_megam_config()
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clf = lambda x: MaxentClassifier.train(featurized_train_set, algorithm)
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elif algorithm in ['GIS', 'IIS']: # Use default GIS/IIS MaxEnt algorithm
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clf = MaxentClassifier.train(featurized_train_set, algorithm)
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else:
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err_msg = str(
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"RTEClassifier only supports these algorithms:\n "
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"'megam', 'BFGS', 'GIS', 'IIS'.\n"
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)
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raise Exception(err_msg)
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print('Testing classifier...')
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acc = accuracy(clf, featurized_test_set)
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print('Accuracy: %6.4f' % acc)
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return clf
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