コード例 #1
0
ファイル: simple_linear_353.py プロジェクト: YuXunLu/nlp
        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()
コード例 #2
0
ファイル: cnn_final.py プロジェクト: xu94-nlp/nlp
        else:
            up_time = 0


#            learning_rate = learning_rate - 0.005
        if (p_rel >= 80.0 or up_time > 4):
            break
        print "this time error", sum_error
        print "former time error", former_sum_error
        print "up_time", up_time
        print "learning_rate", learning_rate
        print "pearson", sci.stats.pearsonr(human_s, machine_s)
        former_sum_error = sum_error
if __name__ == "__main__":
    print "read vector & score"
    word_vectors = nlplib.read_word_vectors("./test_vector/100_3.vec")
    word_pair_score = nlplib.read_csv("./csv/R&G-65.csv")
    ######Read hypernyms
    print "get hypernyms etc."
    for w_pair_score in word_pair_score:
        word_dictionary.append(w_pair_score[0])
        word_dictionary.append(w_pair_score[1])
#   remove dumplicated word, for we are searching hypernyms, hyponyms, synonyms according to the dictionary.
    word_dictionary = list(set(word_dictionary))
    for w in word_dictionary:
        word_hypernyms[w] = nlplib.read_hypernyms(w)
        word_hyponyms[w] = nlplib.read_hyponyms(w)
        word_synonyms[w] = nlplib.read_synonyms(w)
    print "start training"
    cnn_training()
コード例 #3
0
    result = 0.5 * result
    return result


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():
コード例 #4
0
ファイル: cnn_sense.py プロジェクト: YuXunLu/nlp
    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
        if ( word_vectors.has_key(w) ):
            print "has a vector"
        else:
            print "no vector for it"

    for w in vocab:
        word_final_vectors[w] = get_pooling(w)
    training_sense_vectors()
    #calculate pearson similarity
    test_sense_vectors()