Source code for nltk.classify.rte_classify

# Natural Language Toolkit: RTE Classifier
# Copyright (C) 2001-2015 NLTK Project
# Author: Ewan Klein <>
# URL: <>
# For license information, see LICENSE.TXT

Simple classifier for RTE corpus.

It calculates the overlap in words and named entities between text and
hypothesis, and also whether there are words / named entities in the
hypothesis which fail to occur in the text, since this is an indicator that
the hypothesis is more informative than (i.e not entailed by) the text.

TO DO: better Named Entity classification
TO DO: add lemmatization
from __future__ import print_function

import nltk
from nltk.classify.util import accuracy

def ne(token):
    This just assumes that words in all caps or titles are
    named entities.

    :type token: str
    if token.istitle() or token.isupper():
        return True
    return False

def lemmatize(word):
    Use morphy from WordNet to find the base form of verbs.
    lemma = nltk.corpus.wordnet.morphy(word, pos=nltk.corpus.wordnet.VERB)
    if lemma is not None:
        return lemma
    return word

[docs]class RTEFeatureExtractor(object): """ This builds a bag of words for both the text and the hypothesis after throwing away some stopwords, then calculates overlap and difference. """
[docs] def __init__(self, rtepair, stop=True, lemmatize=False): """ :param rtepair: a ``RTEPair`` from which features should be extracted :param stop: if ``True``, stopwords are thrown away. :type stop: bool """ self.stop = stop self.stopwords = set(['a', 'the', 'it', 'they', 'of', 'in', 'to', 'is', 'have', 'are', 'were', 'and', 'very', '.', ',']) self.negwords = set(['no', 'not', 'never', 'failed', 'rejected', 'denied']) # Try to tokenize so that abbreviations like U.S.and monetary amounts # like "$23.00" are kept as tokens. from nltk.tokenize import RegexpTokenizer tokenizer = RegexpTokenizer('([A-Z]\.)+|\w+|\$[\d\.]+') #Get the set of word types for text and hypothesis self.text_tokens = tokenizer.tokenize(rtepair.text) self.hyp_tokens = tokenizer.tokenize(rtepair.hyp) self.text_words = set(self.text_tokens) self.hyp_words = set(self.hyp_tokens) if lemmatize: self.text_words = set(lemmatize(token) for token in self.text_tokens) self.hyp_words = set(lemmatize(token) for token in self.hyp_tokens) if self.stop: self.text_words = self.text_words - self.stopwords self.hyp_words = self.hyp_words - self.stopwords self._overlap = self.hyp_words & self.text_words self._hyp_extra = self.hyp_words - self.text_words self._txt_extra = self.text_words - self.hyp_words
[docs] def overlap(self, toktype, debug=False): """ Compute the overlap between text and hypothesis. :param toktype: distinguish Named Entities from ordinary words :type toktype: 'ne' or 'word' """ ne_overlap = set(token for token in self._overlap if ne(token)) if toktype == 'ne': if debug: print("ne overlap", ne_overlap) return ne_overlap elif toktype == 'word': if debug: print("word overlap", self._overlap - ne_overlap) return self._overlap - ne_overlap else: raise ValueError("Type not recognized:'%s'" % toktype)
[docs] def hyp_extra(self, toktype, debug=True): """ Compute the extraneous material in the hypothesis. :param toktype: distinguish Named Entities from ordinary words :type toktype: 'ne' or 'word' """ ne_extra = set(token for token in self._hyp_extra if ne(token)) if toktype == 'ne': return ne_extra elif toktype == 'word': return self._hyp_extra - ne_extra else: raise ValueError("Type not recognized: '%s'" % toktype)
[docs]def rte_features(rtepair): extractor = RTEFeatureExtractor(rtepair) features = {} features['alwayson'] = True features['word_overlap'] = len(extractor.overlap('word')) features['word_hyp_extra'] = len(extractor.hyp_extra('word')) features['ne_overlap'] = len(extractor.overlap('ne')) features['ne_hyp_extra'] = len(extractor.hyp_extra('ne')) features['neg_txt'] = len(extractor.negwords & extractor.text_words) features['neg_hyp'] = len(extractor.negwords & extractor.hyp_words) return features
[docs]def rte_classifier(trainer, features=rte_features): """ Classify RTEPairs """ train = ((pair, pair.value) for pair in nltk.corpus.rte.pairs(['rte1_dev.xml', 'rte2_dev.xml', 'rte3_dev.xml'])) test = ((pair, pair.value) for pair in nltk.corpus.rte.pairs(['rte1_test.xml', 'rte2_test.xml', 'rte3_test.xml'])) # Train up a classifier. print('Training classifier...') classifier = trainer([(features(pair), label) for (pair, label) in train]) # Run the classifier on the test data. print('Testing classifier...') acc = accuracy(classifier, [(features(pair), label) for (pair, label) in test]) print('Accuracy: %6.4f' % acc) # Return the classifier return classifier
def demo_features(): pairs = nltk.corpus.rte.pairs(['rte1_dev.xml'])[:6] for pair in pairs: print() for key in sorted(rte_features(pair)): print("%-15s => %s" % (key, rte_features(pair)[key])) def demo_feature_extractor(): rtepair = nltk.corpus.rte.pairs(['rte3_dev.xml'])[33] extractor = RTEFeatureExtractor(rtepair) print(extractor.hyp_words) print(extractor.overlap('word')) print(extractor.overlap('ne')) print(extractor.hyp_extra('word')) def demo(): import nltk try: nltk.config_megam('/usr/local/bin/megam') trainer = lambda x: nltk.MaxentClassifier.train(x, 'megam') except ValueError: try: trainer = lambda x: nltk.MaxentClassifier.train(x, 'BFGS') except ValueError: trainer = nltk.MaxentClassifier.train nltk.classify.rte_classifier(trainer) if __name__ == '__main__': demo_features() demo_feature_extractor() demo()