Exemple #1
0
if __name__ == "__main__":
    print "VECTOR:", VECTOR_NAME
    word_vectors = nlp.read_word_vectors(VECTOR_DIR + VECTOR_NAME)
    word_pairs = nlp.read_csv(CSV_DIR + CSV_NAME)
    print "CSV", CSV_NAME
    vocab = []
    for p in word_pairs:
        vocab.append(p[0].lower())
        vocab.append(p[1].lower())
    vocab = list(set(vocab))
    for w in vocab:
        word_hypernyms[w] = nlp.read_hypernyms(w)
        word_hyponyms[w] = nlp.read_hyponyms(w)
        word_synonyms[w] = nlp.read_synonyms(w)
        senses[w] = nlp.read_senses(w)
        for s in senses[w]:
            sense_vectors[s] = np.zeros(VECTOR_DIM)
            sense_hypernyms[s] = nlp.read_hypernyms_by_sense(s)
            sense_hyponyms[s] = nlp.read_hyponyms_by_sense(s)
            sense_synonyms[s] = nlp.read_synonyms_by_sense(s)
            sense_pool[s] = nlp.get_pooling(s, sense_hypernyms, sense_synonyms,
                                            sense_hyponyms, word_vectors,
                                            VECTOR_DIM)
            if (word_vectors.has_key(w)):
                sense_pool[s] = sense_pool[s] + word_vectors[w]
            sense_vectors[s] = sense_pool[s]
            for l in s.lemmas():
                word = str(l.name())
                word_hypernyms[word] = nlp.read_hypernyms(word)
                word_hyponyms[word] = nlp.read_hyponyms(word)
Exemple #2
0
        else:
            continue
        if ( m_score > 0.0 ):
            machine_score.append(m_score)
            human_score.append(float(p[2]))
    p_val, p_rel = sci.stats.spearmanr(human_score, machine_score)
    print "Simple Linear Approach", p_val
if __name__ == "__main__":
    word_vectors = nlp.read_word_vectors(VECTOR_DIR + VECTOR_NAME)
    word_pairs = nlp.read_csv(CSV_DIR + CSV_NAME)
    vocab = []
    for p in word_pairs:
        vocab.append(p[0].lower())
        vocab.append(p[1].lower())
    vocab = list(set(vocab))
    for w in vocab:
        word_hypernyms[w] = nlp.read_hypernyms(w)
        word_hyponyms[w] = nlp.read_hyponyms(w)
        word_synonyms[w] = nlp.read_synonyms(w)
        senses[w] = nlp.read_senses(w)
        for s in senses[w]:
            sense_vectors[s] = np.zeros(VECTOR_DIM)
            sense_hypernyms[s] = nlp.read_hypernyms_by_sense(s)
            sense_hyponyms[s] = nlp.read_hyponyms_by_sense(s)
            sense_synonyms[s] = nlp.read_synonyms_by_sense(s)
            sense_vectors[s] = nlp.get_pooling(s, sense_hypernyms,sense_synonyms,sense_hyponyms, word_vectors, VECTOR_DIM)
            if ( word_vectors.has_key(w)):
                sense_vectors[s] = sense_vectors[s] + word_vectors[w]
        word_pool[w] = nlp.get_pooling(w, word_hypernyms, word_synonyms, word_hyponyms, word_vectors, VECTOR_DIM)
    test_sense_vectors()
Exemple #3
0
        human_score.append(h_s)
    #calc pearson
    p_rel, p_val = sci.stats.spearmanr(human_score, machine_score)
    print "My Approach Spearman Correlation Factor:",p_rel

if __name__ == "__main__":
    word_pairs = nlp_lib.read_csv( CSV_DIR + CSV_NAME )
    for w_pair in word_pairs:
        vocab.append( w_pair[0].lower() )
        vocab.append( w_pair[1].lower() )
    #remove duplicated words in csv file
    vocab = list(set(vocab))
    word_vectors = nlp_lib.read_word_vectors(VECTOR_DIR + VECTOR_NAME)
    #read word senses
    for w in vocab:
        word_senses[w] = nlp_lib.read_senses(w)
    #read word senses' hyponyms
    for w in vocab:
        for s in word_senses[w]:
            word_sense_hyponyms[s] = nlp_lib.read_hyponyms_by_sense(s)
            word_sense_hypernyms[s] = nlp_lib.read_hypernyms_by_sense(s)
            word_sense_synonyms[s] = nlp_lib.read_synonyms_by_sense(s)
            word_sense_full_pooling[s] = get_full_pooling_sense(s, w)
    #read for retrofitting
    for w in vocab:
        word_hyponyms[w] = nlp_lib.read_hyponyms(w)
        word_hypernyms[w] = nlp_lib.read_hypernyms(w)
        word_synonyms[w] = nlp_lib.read_synonyms(w)

    for w in vocab:
        print "word",w