From c2418159593e6208d3849cf2dfb49abc6a6638ea Mon Sep 17 00:00:00 2001
From: lidunwei <13564180096@163.com>
Date: Wed, 7 Oct 2020 23:11:01 +0800
Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0word2vec=E8=AE=AD=E7=BB=83?=
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.idea/workspace.xml | 24 ++++++++++------
src/medical_word2vec.py | 64 +++++++++++++++++++++++++++++++++++++++++
2 files changed, 79 insertions(+), 9 deletions(-)
create mode 100644 src/medical_word2vec.py
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
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+++ b/.idea/workspace.xml
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diff --git a/src/medical_word2vec.py b/src/medical_word2vec.py
new file mode 100644
index 0000000..ff91b20
--- /dev/null
+++ b/src/medical_word2vec.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import jieba
+import warnings
+import logging
+import os.path
+import sys
+import multiprocessing
+
+from gensim.models import Word2Vec
+from gensim.models.word2vec import LineSentence
+filePath = 'corpus_1.txt'
+fileSegWordDonePath = 'corpusSegDone_1.txt'
+warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')
+
+
+# 打印中文列表
+def PrintListChinese(list):
+ for i in range(len(list)):
+ print(list[i])
+
+
+fileTrainRead = []
+with open(filePath, 'r') as fileTrainRaw:
+ for line in fileTrainRaw: # 按行读取文件
+ fileTrainRead.append(line)
+
+# jieba分词后保存在列表中
+fileTrainSeg = []
+for i in range(len(fileTrainRead)):
+ fileTrainSeg.append([' '.join(list(jieba.cut(fileTrainRead[i][9:-11], cut_all=False)))])
+ if i % 100 == 0:
+ print(i)
+
+# 保存分词结果到文件中
+with open(fileSegWordDonePath, 'w', encoding='utf-8') as fW:
+ for i in range(len(fileTrainSeg)):
+ fW.write(fileTrainSeg[i][0])
+ fW.write('\n')
+
+"""
+gensim word2vec获取词向量
+"""
+
+if __name__ == '__main__':
+ program = os.path.basename(sys.argv[0]) # 读取当前文件的文件名
+ logger = logging.getLogger(program)
+ logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s', level=logging.INFO)
+ logger.info("running %s" % ' '.join(sys.argv))
+
+ # inp为输入语料, outp1为输出模型, outp2为vector格式的模型
+ inp = 'corpusSegDone_1.txt'
+ out_model = 'corpusSegDone_1.model'
+ out_vector = 'corpusSegDone_1.vector'
+
+ # 训练skip-gram模型
+ model = Word2Vec(LineSentence(inp), size=50, window=5, min_count=5,
+ workers=multiprocessing.cpu_count())
+
+ # 保存模型
+ model.save(out_model)
+ # 保存词向量
+ model.wv.save_word2vec_format(out_vector, binary=False)