Пример #1
0
def test_sim_graph():
    from sematch.semantic.graph import SimGraph
    from sematch.semantic.similarity import WordNetSimilarity
    from sematch.nlp import Extraction, lemmatization
    from sematch.sparql import EntityFeatures
    from collections import Counter
    madrid = EntityFeatures().features('http://dbpedia.org/resource/Tom_Cruise')
    words = Extraction().extract_words_sent(madrid['abstract'])
    words = list(set(lemmatization(words)))
    wns = WordNetSimilarity()
    word_graph = SimGraph(words, wns.word_similarity)
    word_scores = word_graph.page_rank()
    words, scores =zip(*Counter(word_scores).most_common(10))
    assert words is not None
Пример #2
0
def test_sim_graph():
    from sematch.semantic.graph import SimGraph
    from sematch.semantic.similarity import WordNetSimilarity
    from sematch.nlp import Extraction, lemmatization
    from sematch.sparql import EntityFeatures
    from collections import Counter
    madrid = EntityFeatures().features(
        'http://dbpedia.org/resource/Tom_Cruise')
    words = Extraction().extract_words_sent(madrid['abstract'])
    words = list(set(lemmatization(words)))
    wns = WordNetSimilarity()
    word_graph = SimGraph(words, wns.word_similarity)
    word_scores = word_graph.page_rank()
    words, scores = zip(*Counter(word_scores).most_common(10))
    assert words is not None
Пример #3
0
 def disambiguate_graph(self, sentence):
     words_origin = word_tokenize(sentence)
     #extract words that have a synset in WordNet, currently support NOUN.
     words = [w for w in words_origin if self._wn_sim.word2synset(w)]
     # map words to synsets
     words_synsets = {w:self._wn_sim.word2synset(w) for w in words}
     # construct sets list
     synsets = list(itertools.chain.from_iterable([words_synsets[w] for w in words]))
     # remove duplicate synsets
     synsets = list(set(synsets))
     # define semantic similarity metric
     sim_metric = lambda x, y: self._wn_sim.similarity(x, y, self._sim_name)
     # construct similarity graphs
     sim_graph = SimGraph(synsets, sim_metric)
     # get pagerank scores of synsets
     rank_scores = sim_graph.page_rank()
     results = []
     for w in words_origin:
         if w in words:
             candidate_scores = {s:rank_scores[s] for s in words_synsets[w]}
             results.append((w, Counter(candidate_scores).most_common(1)[0][0]))
         else:
             results.append((w, None))
     return results