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用Python写一个简朴的中文分词器

2017-11-02 08:00 星期四 所属: Python教程 浏览:58

解压后取出以下文件:

练习数据:icwb2-data/training/pku_ training.utf8

测试数据:icwb2-data/testing/pku_ test.utf8

正确分词功效:icwb2-data/gold/pku_ test_ gold.utf8

评分东西:icwb2-data/script/socre

2 算法描写

算法是最简朴的正向最大匹配(FMM):

用练习数据生成一个字典

对测试数据从左到右扫描,碰着一个最长的词,就切分下来,直到句子竣事

注:这是最初的算法,这样做代码可以节制在60行内,厥后看测试功效发明没有很好地处理惩罚数字问题, 才又增加了对数字的处理惩罚。

3 源代码及注释

#! /usr/bin/env python
# -*- coding: utf-8 -*-
  
# Author: minix
# Date:   2013-03-20

   
import codecs
import sys
   
# 由法则处理惩罚的一些非凡标记
numMath = [u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9']
numMath_suffix = [u'.', u'%', u'亿', u'万', u'千', u'百', u'十', u'个']
numCn = [u'一', u'二', u'三', u'四', u'五', u'六', u'七', u'八', u'九', u'〇', u'零']
numCn_suffix_date = [u'年', u'月', u'日']
numCn_suffix_unit = [u'亿', u'万', u'千', u'百', u'十', u'个']
special_char = [u'(', u')']
   
   
def proc_num_math(line, start):
    """ 处理惩罚句子中呈现的数学标记 """
    oldstart = start
    while line[start] in numMath or line[start] in numMath_suffix:
        start = start + 1
    if line[start] in numCn_suffix_date:
        start = start + 1
    return start - oldstart
   
def proc_num_cn(line, start):
    """ 处理惩罚句子中呈现的中文数字 """
    oldstart = start
    while line[start] in numCn or line[start] in numCn_suffix_unit:
        start = start + 1
    if line[start] in numCn_suffix_date:
        start = start + 1
    return start - oldstart
   
def rules(line, start):
    """ 处理惩罚非凡法则 """
    if line[start] in numMath:
        return proc_num_math(line, start)
    elif line[start] in numCn:
        return proc_num_cn(line, start)
   
def genDict(path):
    """ 获取辞书 """
    f = codecs.open(path,'r','utf-8')
    contents = f.read()
    contents = contents.replace(u'\r', u'')
    contents = contents.replace(u'\n', u'')
    # 将文件内容按空格分隔
    mydict = contents.split(u' ')
    # 去除辞书List中的反复
    newdict = list(set(mydict))
    newdict.remove(u'')
   
    # 成立辞书
    # key为词首字,value为以此字开始的词组成的List
    truedict = {}
    for item in newdict:
        if len(item)>0 and item[0] in truedict:
            value = truedict[item[0]]
            value.append(item)
            truedict[item[0]] = value
        else:
            truedict[item[0]] = [item]
    return truedict
   
def print_unicode_list(uni_list):
    for item in uni_list:
        print item,
   
def divideWords(mydict, sentence):
    """ 
    按照辞书对句子举办分词,
    利用正向匹配的算法,从左到右扫描,碰着最长的词,
    就将它切下来,直到句子被支解完闭
    """
    ruleChar = []
    ruleChar.extend(numCn)
    ruleChar.extend(numMath)
    result = []
    start = 0
    senlen = len(sentence)
    while start < senlen:
        curword = sentence[start]
        maxlen = 1
        # 首先查察是否可以匹配非凡法则
        if curword in numCn or curword in numMath:
            maxlen = rules(sentence, start)
        # 寻找以当前字开头的最长词
        if curword in mydict:
            words = mydict[curword]
            for item in words:
                itemlen = len(item)
                if sentence[start:start+itemlen] == item and itemlen > maxlen:
                    maxlen = itemlen
        result.append(sentence[start:start+maxlen])
        start = start + maxlen
    return result
   
def main():
    args = sys.argv[1:]
    if len(args) < 3:
        print 'Usage: python dw.py dict_path test_path result_path'
        exit(-1)
    dict_path = args[0]
    test_path = args[1]
    result_path = args[2]
   
    dicts = genDict(dict_path)
    fr = codecs.open(test_path,'r','utf-8')
    test = fr.read()
    result = divideWords(dicts,test)
    fr.close()
    fw = codecs.open(result_path,'w','utf-8')
    for item in result:
        fw.write(item + ' ')
    fw.close()
   
if __name__ == "__main__":
    main()

4 测试及评分功效

利用 dw.py 练习数据 测试数据, 生成功效文件

利用 score 按照练习数据,正确分词功效,和我们生成的功效举办评分

利用 tail 查察功效文件最后几行的总体评分,别的socre.utf8中还提供了大量的较量功效, 可以用于发明本身的分词功效在哪儿做的不足好

注:整个测试进程都在Ubuntu下完成

$ python dw.py pku_training.utf8 pku_test.utf8 pku_result.utf8

$ perl score pku_training.utf8 pku_test_gold.utf8 pku_result.utf8 > score.utf8

$ tail -22 score.utf8

INSERTIONS:     0

DELETIONS:      0

SUBSTITUTIONS:  0

NCHANGE:        0

NTRUTH: 27

NTEST:  27

TRUE WORDS RECALL:      1.000

TEST WORDS PRECISION:   1.000

=== SUMMARY:

=== TOTAL INSERTIONS:   4623

=== TOTAL DELETIONS:    1740

=== TOTAL SUBSTITUTIONS:        6650

=== TOTAL NCHANGE:      13013

=== TOTAL TRUE WORD COUNT:      104372

=== TOTAL TEST WORD COUNT:      107255

=== TOTAL TRUE WORDS RECALL:    0.920

=== TOTAL TEST WORDS PRECISION: 0.895

=== F MEASURE:  0.907

=== OOV Rate:   0.940

=== OOV Recall Rate:    0.917

=== IV Recall Rate:     0.966

#p#分页标题#e#

基于辞书的FMM算法长短常基本的分词算法,结果没那么好,不外足够简朴,也易于入手,跟着进修的深入,我大概还会用Python实现其它的分词算法。别的一个感觉是,看书的时候只管多去实现,这样会让你有足够的热情去存眷理论的每一个细节,不会感想那么枯燥无力。

 

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