中文分词：采用二元词图以及viterbi算法（二）

 中文分词：采用二元词图以及Viterbi算法（三）

中文分词：采用二元词图以及Viterbi算法（一）

1。首先建立词典。此处词典要理解为：对训练语料库中的词进行词频等信息的统计后形成的数据结构，和“新华字典”中的字典意义不一样。我的实现中建立了两个词典：“单词”词典统计每个词的出现次数，“双词”词典统计每两个词连续出现的次数(因为采用的是二元语法模型)。然后又分别对单词词典，双词词典形成一级Trie树，或者叫做“带首字索引”的字典。

代码如下

# -*- coding: cp936 -*-
import re
import cPickle as mypickle
def datafile(name,sep='|'):
    for line in file(name):
        yield line.split(sep)
candidates=datafile(r'C:\Python26\Bigramwordsegemtation\data\training.txt')
p1=re.compile('(^\s+|\s+$)')
p2=re.compile('\d')
#p3=re.compile('\s+')
mySingleWordDict={}
#myDoubleWordDict={}
for m in candidates:
    #singleline=[]
    for e in m:
        e=p1.sub('',e)
        if p2.match(e):
            #e=p3.sub('_',e)
            mySingleWordDict[e]=float(mySingleWordDict.get(e,0)+1)
            print '词为%s,个数为%s'%(e,mySingleWordDict[e])

N=sum(mySingleWordDict.itervalues())
for  key in mySingleWordDict.iterkeys():
    mySingleWordDict[key]=mySingleWordDict[key]/N
#for  m in mySingleWordDict.iteritems():
    #print m
            
        
fid=file('SingleWordDictionaryCrossValidation.dat','w')
mypickle.dump(mySingleWordDict,fid)
fid.close()
print 'finish'
print N

# -*- coding: cp936 -*-
import re
import cPickle as mypickle

delimiter='|'
def datafile(name,sep='|'):
    '''use generator to create a iterable object '''
    for line in file(name):
        yield line.split(sep)
candidates=datafile(r'c:\python26\Bigramwordsegemtation\data\training.txt')
p1=re.compile('(^\s+|\s+$)')
p2=re.compile('\d')
myDoubleWordDict={}
for m in candidates:
    singleline=[]
    for e in m:
        e=p1.sub('',e)
        if p2.match(e):
            singleline.append(e)
    if len(singleline)>=2:
        initial=singleline[0]+delimiter+'S'
        myDoubleWordDict[initial]=float(myDoubleWordDict.get(initial,0)+1)
        print '词为%s,个数为%s'%(initial,myDoubleWordDict[initial])
        for i in range(0,len(singleline)-1):
             c=delimiter.join(singleline[i:i+2])
             myDoubleWordDict[c]=float(myDoubleWordDict.get(c,0)+1)
             print '词为%s,个数为%s'%(c,myDoubleWordDict[c])
             
                
N=sum(myDoubleWordDict.itervalues())
for  key in myDoubleWordDict.iterkeys():
    myDoubleWordDict[key]=myDoubleWordDict[key]/N
#for  m in myDoubleWordDict.iteritems():
   # print m

#print N
            
fid=file('DoubleWordDictionaryCrossValidation2.dat','w')
mypickle.dump(myDoubleWordDict,fid)
fid.close()
print 'finish'
print N



                
                
            

# -*- coding: cp936 -*-
import re
import cPickle as mypickle
p=re.compile('\d+')
myDict=mypickle.load(file('DoubleWordDictionaryCrossValidation.dat'))
myTrie={}
#二级Trie树，词按首字归类
for key in myDict.iterkeys():
    tmp=p.findall(key)
    if myTrie.get(tmp[0])==None:
        myTrie[tmp[0]]={}
for (key,val) in myDict.iteritems():
    tmp=p.findall(key)
    myTrie[tmp[0]][key]=val
    print '一级键%s二级键%s值%s'%(tmp[0],key,val)

fid=file('myDoubleWordTrieCrossValidation.dat','w')
mypickle.dump(myTrie,fid)
fid.close()
print 'finish'

 下一部分是主算法模块，在主算法模块，我们调用的数据结构为“单词”的一级Trie树词典，与“双词”的一级Trie树字典