Source code for nlp_architect.data.cdc_resources.wordnet.wordnet_online

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import nltk
from nltk.corpus import wordnet as wn

from nlp_architect.data.cdc_resources.data_types.wn.wordnet_page import WordnetPage
from nlp_architect.utils.string_utils import StringUtils


[docs]class WordnetOnline(object): def __init__(self): self.cache = dict() nltk.download('wordnet')
[docs] def get_pages(self, mention): if mention.tokens_str in self.cache: return self.cache[mention.tokens_str] head_synonyms, head_names_derivationally = self.extract_synonyms_and_derivation( mention.mention_head) head_lemma_synonyms, head_lemma_derivationally = self.extract_synonyms_and_derivation( mention.mention_head_lemma) clean_phrase = StringUtils.normalize_str(mention.tokens_str) all_clean_words_synonyms = self.all_clean_words_synonyms(clean_phrase) wordnet_page = WordnetPage(mention.tokens_str, clean_phrase, mention.mention_head, mention.mention_head_lemma, head_synonyms, head_lemma_synonyms, head_names_derivationally, head_lemma_derivationally, all_clean_words_synonyms) self.cache[mention.tokens_str] = wordnet_page return wordnet_page
[docs] @staticmethod def extract_synonyms_and_derivation(word): lemma_names = set() derivationally_related_forms = set() for synset in wn.synsets(word): for lemma in synset.lemmas(): lemma_name = lemma.name().replace('_', ' ') if not StringUtils.is_stop(lemma_name.lower()): lemma_names.add(lemma_name) derivationally_related_forms.update( [l.name().replace('_', ' ') for l in lemma.derivationally_related_forms() if not StringUtils.is_stop(l.name().lower())]) return lemma_names, derivationally_related_forms
[docs] @staticmethod def all_clean_words_synonyms(clean_phrase): words = clean_phrase.split() return [set([lemma.lower().replace('_', ' ') for synset in wn.synsets(w) for lemma in synset.lemma_names() if not StringUtils.is_stop(lemma.lower())]) for w in words]