Python 中文分词：jieba库的使用

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发布于 2022-11-15 15:21:40

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发布于 2022-11-15 15:21:40

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Python 中文分词：jieba库的使用

1.jieba库的安装

jieba是Python中一个重要的第三方中文分词函数库，需要通过pip指令安装：

pip install jieba   
# 或者 
pip3 install jieba

2.常用函数方法

jieba库的常用函数方法如下：

函数	描述
jieba.cut(s)	精确模式，返回一个可迭代的数据类型
jieba.cut(s, cut_all=True)	全模式，输出文本s中所有可能单词
jieba.cut_for_search(s)	搜索引擎模式，适合搜索引擎建立索引的分词结果
jieba.lcut(s)	精确模式，返回一个列表类型，建议使用
jieba.lcut(s, cut_all=True)	全模式，返回一个列表类型，建议使用
jieba.add_word(w)	向分词词典中增加新词w

代码实例：

import jieba
print(jieba.lcut('Python是一种十分便捷的编程语言'))
print(jieba.lcut('Python是一种十分便捷的编程语言', cut_all=True))
print(jieba.lcut_for_search('Python是一种十分便捷的编程语言'))

3.jieba库的应用：文本词频统计

3.1 《The Old Man And the Sea》英文词频统计

import jieba
def getText():
    txt = open("Documents/《The Old Man And the Sea》.txt", "r", encoding='utf-8').read()
    txt = txt.lower()
    for ch in '!"#$%&()*+,-./:;<=>?@[\\]^_‘{|}~':
        txt = txt.replace(ch, " ")   #将文本中特殊字符替换为空格
    return txt

words  = getText().split()
counts = {}
for word in words:
    counts[word] = counts.get(word,0) + 1
items = list(counts.items())
items[:10]
items.sort(key=lambda x:x[1], reverse=True) 
for i in range(10):
    word, count = items[i]
    print ("{0:<10}{1:>5}".format(word, count))
# 输出：
# the        2751
# and        1458
# he         1221
# of          788
# to          555
# a           538
# it          528
# his         513
# in          503
# i           472

观察输出结果可以看到，高频单词大多数是冠词、代词、连接词等语法型词汇，并不能代表文章的含义。进一步，可以采用集合类型构建一个排除词汇库excludes，在输出结果中排除这个词汇库中内容。

excludes = {"the","and","of","you","a","i","my","in","he","to","it","his","was",
            "that","is","but","him","as","on","not","with","had","said","now","for",
           "thought","they","have","then","were","from","could","there","out","be",
           "when","at","them","all","will","would","no","do","are","or","down","so",
            "up","what","if","back","one","can","must","this","too","more","again",
           "see","great","two"}

def getText():
    txt = open("Documents/《The Old Man And the Sea》.txt", "r", encoding='utf-8').read()
    txt = txt.lower()
    for ch in '!"#$%&()*+,-./:;<=>?@[\\]^_‘{|}~“”':
        txt = txt.replace(ch, " ")   #将文本中特殊字符替换为空格
    return txt

words  = getText().split()
counts = {}
for word in words:
    counts[word] = counts.get(word,0) + 1
for word in excludes:
    del(counts[word])
items = list(counts.items())
items[:10]
items.sort(key=lambda x:x[1], reverse=True) 
for i in range(10):
    word, count = items[i]
    print ("{0:<10}{1:>5}".format(word, count))
# 输出：
# old         300
# man         298
# fish        281
# line        139
# water       107
# boy         105
# hand         91
# sea          67
# head         65
# come         60

3.2 《水浒传》人物出场统计

import jieba

txt = open("Documents/《水浒传》.txt", "r", encoding='utf-8').read()
words  = jieba.lcut(txt)
counts = {}
for word in words:
    if len(word) == 1:  #排除单个字符的分词结果
        continue
    counts[word] = counts.get(word,0) + 1
items = list(counts.items())
items.sort(key=lambda x:x[1], reverse=True) 
for i in range(15):
    word, count = items[i]
    print ("{0:<10}{1:>5}".format(word, count))
# 输出：
# 宋江         2538
# 两个         1733
# 一个         1399
# 李逵         1117
# 武松         1053
# 只见          917
# 如何          911
# 那里          858
# 哥哥          750
# 说道          729
# 林冲          720
# 军马          719
# 头领          707
# 吴用          654
# 众人          652

观察输出，我们发现结果中有非人名词汇，与英文词频统计类似，我们需要排除一些人名无关词汇。

import jieba
excludes = {'两个','一个','只见','如何','那里','哥哥','说道','军马',
           '头领','众人','这里','兄弟','出来','小人','梁山泊','这个',
           '今日','妇人','先锋','好汉','便是','人马','问道','起来',
           '甚么','因此','却是','我们','正是','三个','如此','且说',
           '不知','不是','只是','次日','不曾','呼延','不得','一面',
           '看时','不敢','如今','来到','当下','原来','将军','山寨',
           '喝道','兄长','只得','军士','里面','大喜','天子','一齐',
           '知府','性命','商议','小弟','那个','公人','将来','前面',
            '东京','喽罗','那厮','城中','弟兄','下山','不见','怎地',
            '上山','随即','不要'}



txt = open("Documents/《水浒传》.txt", "r", encoding='utf-8').read()
words  = jieba.lcut(txt)
counts = {}
for word in words:
    if len(word) == 1:
        continue
    elif word == "宋江道":
        rword = "宋江"
    else:
        rword = word
    counts[rword] = counts.get(rword,0) + 1
for word in excludes:
    del(counts[word])
items = list(counts.items())
items.sort(key=lambda x:x[1], reverse=True) 
for i in range(15):
    word, count = items[i]
    print ("{0:<10}{1:>5}".format(word, count))
# 输出：
# 宋江         3010
# 李逵         1117
# 武松         1053
# 林冲          720
# 吴用          654
# 卢俊义         546
# 鲁智深         356
# 戴宗          312
# 柴进          301
# 公孙胜         272
# 花荣          270
# 秦明          258
# 燕青          252
# 朱仝          245
# 晁盖          238

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原始发表：2022-11-08，如有侵权请联系 cloudcommunity@tencent.com 删除

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