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 LineSentencedef 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 linesexcept: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 = 0for line in lines:num = num + 1if 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_filedef 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 modeldef 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_wordsdef load_models(model_path):return gensim.models.Word2Vec.load(model_path)if "__name__ == __main__()":filename = "d:\\data\\dk_mainsuit_800w.txt" #source fileuser_dic_file = "new_dict.txt" # user dic fileseparated_file = "d:\\data\\dk_spe_file_20170216.txt" # separeted words filemodel_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 againbuild_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)