rte_classify.py 文件源码

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