word2vct算法实现

本篇文章主要是实现python 自然语言处理包 gensim 中用于词向量建模的 word2vec算法。

示例代码如下：

# encoding=utf-8import loggingimport sysfrom gensim.models import Word2Vecif __name__ == '__main__':    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)    if len(sys.argv) < 3:        sys.exit(1)    outputFile1, outputFile2 = sys.argv[1:3]    sentences = [        "I think that most of us know by now that water is essential to our survival We’ve probably also all heard doctors say that drinking roughly eight glasses a day is ideal",        "yoyoyo you go home now to sleep"]    vocab = [s.encode('utf-8').decode().split() for s in sentences]    #建立模型    model = Word2Vec(sentences, size=100, window=5, min_count=5, workers=4)    #保存模型    model.save(outputFile1)    model.save_word2vec_format(outputFile2, binary=False)

#测试模型# encoding='utf-8'import loggingimport sysfrom gensim.models import Word2Vecif __name__ == '__main__':    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)    if len(sys.argv) < 3:        sys.exit(1)    file, word = sys.argv[1:3]    #从磁盘文件 file 加载模型    model = Word2Vec.load_word2vec_format(file, binary=False)    print(model.most_similar(word))

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