wsd.py 文件源码

def disambiguate_word(self, sentence, index):
        super().disambiguate_word(sentence, index)

        lemmas = self.lemmatize(sentence)

        if index not in lemmas:
            return

        svector = self.sensegram(lemmas.values()) # sentence vector

        if svector is None:
            return

        # map synset identifiers to the cosine similarity value
        candidates = Counter({id: sim(svector, self.dense[id]).item(0)
                              for id in self.inventory.index[lemmas[index]]
                              if self.dense[id] is not None})

        if not candidates:
            return

        for id, _ in candidates.most_common(1):
            return id