Word2Vec训练同义词模型

2024-06-29 11:35| 来源: 网络整理| 查看: 265

一、需求描述

业务需求的目标是识别出目标词汇的同义词和相关词汇，如下为部分目标词汇(主要用于医疗问诊)：

尿痘痘发冷呼吸困难恶心

数据源是若干im数据，那么这里我们选择google 的word2vec模型来训练同义词和相关词。

二、数据处理

数据处理考虑以下几个方面： 1. 从hive中导出不同数据量的数据 2. 过滤无用的训练样本（例如字数少于5） 3. 准备自定义的词汇表 4. 准备停用词表

三、工具选择

选择python 的gensim库，由于先做预研，数据量不是很大,选择单机就好，暂时不考虑spark训练。后续生产环境计划上spark。

详细的gensim中word2vec文档

上述文档有关工具的用法已经很详细了，就不多说。

分词采用jieba。

四、模型训练步骤简述

1.先做分词、去停用词处理

seg_word_line = jieba.cut(line, cut_all = True)

2.将分词的结果作为模型的输入

model = gensim.models.Word2Vec(LineSentence(source_separated_words_file), size=200, window=5, min_count=5, alpha=0.02, workers=4)

3.保存模型，方便以后调用，获得目标词的同义词

similary_words = model.most_similar(w, topn=10) 五、重要调参目标

比较重要的参数： 1. 训练数据的大小，当初只用了10万数据，训练出来的模型很不好，后边不断地将训练语料增加到800万，效果得到了明显的提升 2. 向量的维度，这是词汇向量的维数，这个会影响到计算，理论上来说维数大一点会好。 3. 学习速率 4. 窗口大小

在调参上，并没有花太多精力，因为目测结果还好，到时上线使用前再仔细调整。

六、模型的实际效果目标词同义词相关词尿尿液,撒尿,尿急,尿尿有,尿到,内裤,尿意,小解,前列腺炎,小便痘痘逗逗,豆豆,痘子,小痘,青春痘,红痘,长痘痘,粉刺,讽刺,白头发冷发烫,没力,忽冷忽热,时冷时热,小柴胡,头昏,嗜睡,38.9,头晕,发寒呼吸困难气来,气紧,窒息,大气,透不过气,出不上,濒死,粗气,压气,心律不齐恶心闷,力气,呕心,胀气,涨,不好受,不进,晕车,闷闷,精神七、可以跑的CODE import codecs import jieba import gensim from gensim.models.word2vec import LineSentence def read_source_file(source_file_name): try: file_reader = codecs.open(source_file_name, 'r', 'utf-8',errors="ignore") lines = file_reader.readlines() print("Read complete!") file_reader.close() return lines except: print("There are some errors while reading.") def write_file(target_file_name, content): file_write = codecs.open(target_file_name, 'w+', 'utf-8') file_write.writelines(content) print("Write sussfully!") file_write.close() def separate_word(filename,user_dic_file, separated_file): print("separate_word") lines = read_source_file(filename) #jieba.load_userdict(user_dic_file) stopkey=[line.strip() for line in codecs.open('stopword_zh.txt','r','utf-8').readlines()] output = codecs.open(separated_file, 'w', 'utf-8') num = 0 for line in lines: num = num + 1 if num% 10000 == 0: print("Processing line number: " + str(num)) seg_word_line = jieba.cut(line, cut_all = True) wordls = list(set(seg_word_line)-set(stopkey)) if len(wordls)>0: word_line = ' '.join(wordls) + '\n' output.write(word_line) output.close() return separated_file def build_model(source_separated_words_file,model_path): print("start building...",source_separated_words_file) model = gensim.models.Word2Vec(LineSentence(source_separated_words_file), size=200, window=5, min_count=5, alpha=0.02, workers=4) model.save(model_path) print("build successful!", model_path) return model def get_similar_words_str(w, model, topn = 10): result_words = get_similar_words_list(w, model) return str(result_words) def get_similar_words_list(w, model, topn = 10): result_words = [] try: similary_words = model.most_similar(w, topn=10) print(similary_words) for (word, similarity) in similary_words: result_words.append(word) print(result_words) except: print("There are some errors!" + w) return result_words def load_models(model_path): return gensim.models.Word2Vec.load(model_path) if "__name__ == __main__()": filename = "d:\\data\\dk_mainsuit_800w.txt" #source file user_dic_file = "new_dict.txt" # user dic file separated_file = "d:\\data\\dk_spe_file_20170216.txt" # separeted words file model_path = "information_model0830" # model file #source_separated_words_file = separate_word(filename, user_dic_file, separated_file) source_separated_words_file = separated_file # if separated word file exist, don't separate_word again build_model(source_separated_words_file, model_path)# if model file is exist, don't buile modl model = load_models(model_path) words = get_similar_words_str('头痛', model) print(words)

【本文地址】

公司简介

联系我们