python语料处理_Python中文语料批量预处理手记

手记实用系列文章：

语料预处理封装类：

#coding=utf-8

import os

import jieba

import sys

import re

import time

import jieba.posseg as pseg

sys.path.append("../")

jieba.load_userdict("../Database/userdict.txt") # 加载自定义分词词典

'''

title：利用结巴分词进行文本语料处理：单文本处理器、批量文件处理器

1 首先对文本进行遍历查找

2 创建原始文本的保存结构

3 对原文本进行结巴分词和停用词处理

4 对预处理结果进行标准化格式，并保存原文件结构路径

author：白宁超

myblog：http://www.cnblogs.com/baiboy/

'''

'''

分词.词性标注以及去停用词

stopwordspath： 停用词路径

dealpath：中文数据预处理文件的路径

savepath：中文数据预处理结果的保存路径

'''

def cutTxtWord(dealpath,savepath,stopwordspath):

stopwords = {}.fromkeys([ line.rstrip() for line in open(stopwordspath,"r",encoding='utf-8')]) # 停用词表

with open(dealpath,"r",encoding='utf-8') as f:

txtlist=f.read() # 读取待处理的文本

words =pseg.cut(txtlist) # 带词性标注的分词结果

cutresult=""# 获取去除停用词后的分词结果

for word, flag in words:

if word not in stopwords:

cutresult += word+"/"+flag+" " #去停用词

getFlag(cutresult,savepath) #

'''

分词.词性标注以及去停用词

stopwordspath： 停用词路径

read_folder_path ：中文数据预处理文件的路径

write_folder_path ：中文数据预处理结果的保存路径

filescount=300 #设置文件夹下文件最多多少个

'''

def cutFileWord(read_folder_path,write_folder_path,stopwordspath):

# 停用词表

stopwords = {}.fromkeys([ line.rstrip() for line in open(stopwordspath,"r",encoding='utf-8')])

# 获取待处理根目录下的所有类别

folder_list = os.listdir(read_folder_path)

# 类间循环

for folder in folder_list:

#某类下的路径

new_folder_path = os.path.join(read_folder_path, folder)

# 创建保存文件目录

path=write_folder_path+folder #保存文件的子文件

isExists=os.path.exists(path)

if not isExists:

os.makedirs(path)

print(path+' 创建成功')

else: pass

save_folder_path = os.path.join(write_folder_path, folder)#某类下的保存路径

print('--> 请稍等，正在处理中...')

# 类内循环

files = os.listdir(new_folder_path)

j = 1

for file in files:

if j > len(files): break

dealpath = os.path.join(new_folder_path, file) #处理单个文件的路径

with open(dealpath,"r",encoding='utf-8') as f:

txtlist=f.read()

# python 过滤中文、英文标点特殊符号

# txtlist1 = re.sub("[\s+\.\!\/_,$%^*(+\"\']+|[+——！，。？、~@#￥%……&*()]+", "",txtlist)

words =pseg.cut(txtlist) # 带词性标注的分词结果

cutresult="" # 单个文本：分词后经停用词处理后的结果

for word, flag in words:

if word not in stopwords:

cutresult += word+"/"+flag+" " #去停用词

savepath = os.path.join(save_folder_path,file)

getFlag(cutresult,savepath)

j += 1

'''

做词性筛选

cutresult：str类型，初切分的结果

savepath： 保存文件路径

'''

def getFlag(cutresult,savepath):

txtlist=[] #过滤掉的词性后的结果

#词列表为自己定义要过滤掉的词性

cixing=["/x","/zg","/uj","/ul","/e","/d","/uz","/y"]

for line in cutresult.split('\n'):

line_list2=re.split('[ ]', line)

line_list2.append("\n") # 保持原段落格式存在

line_list=line_list2[:]

for segs in line_list2:

for K in cixing:

if K in segs:

line_list.remove(segs)

break

else:

pass

txtlist.extend(line_list)

# 去除词性标签

resultlist=txtlist[:]

flagresult=""

for v in txtlist:

if "/" in v:

slope=v.index("/")

letter=v[0:slope]+" "

flagresult+= letter

else:

flagresult+= v

standdata(flagresult,savepath)

'''

标准化处理，去除空行，空白字符等。

flagresult:筛选过的结果

'''

def standdata(flagresult,savepath):

f2=open(savepath,"w",encoding='utf-8')

for line in flagresult.split('\n'):

if len(line)>=2:

line_clean="/ ".join(line.split())

lines=line_clean+" "+"\n"

f2.write(lines)

else: pass

f2.close()

if __name__ == '__main__' :

t1=time.time()

# 测试单个文件

dealpath="../Database/SogouC/FileTest/1.txt"

savepath="../Database/SogouCCut/FileTest/1.txt"

stopwordspath='../Database/stopwords/CH_stopWords.txt'

stopwordspath1='../Database/stopwords/HG_stopWords.txt' # 哈工大停用词表

# 批量处理文件夹下的文件

# rfolder_path = '../Database/SogouC/Sample/'

rfolder_path = '../Database/SogouC/FileNews/'

# 分词处理后保存根路径

wfolder_path = '../Database/SogouCCut/'

# 中文语料预处理器

# cutTxtWord(dealpath,savepath,stopwordspath) # 单文本预处理器

cutFileWord(rfolder_path,wfolder_path,stopwordspath) # 多文本预处理器

t2=time.time()

print("中文语料语处理完成，耗时："+str(t2-t1)+"秒。") #反馈结果

执行结果：