转：CRF++词性标注
CRF++词性标注
训练和测试的语料都是人民日报98年标注语料，训练和测试比例是10：1，直接通过CRF++标注词性的准确率:0.933882。特征有一千多万个，训练时间比较长。机器cpu是48核，通过crf++，指定并线数量 -p为40，训练了大概七个小时才结束。
语料库、生成训练数据的python脚本、训练日志、模型、计算准确率脚本都上传到网盘，可以直接下载：戳我下载 CRF++词性标注，程序在centos6.5+python2.7下面运行通过，如果在win下或者ubuntu下可能会有异常，通常都是编码、路径规范等小问题，通过逐行debug脚本应该很容易找到问题，同时要确定crf++在自己机器本身编译没有问题，下面说一下每一步的过程。
文章目录 [展开]
生成训练和测试数据
生成训练和测试数据脚本：get_post_train_test_data.py，执行过程中会打印出来一些调试信息。
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
|
#coding=utf8
import sys
#home_dir = "D:/source/NLP/people_daily//"
home_dir = "./"
def saveDataFile(trainobj,testobj,isTest,word,handle):
if isTest:
saveTrainFile(testobj,word,handle)
else:
saveTrainFile(trainobj,word,handle)
def saveTrainFile(fiobj,word,handle):
if len(word) > 0 and word != "。" and word != "，":
fiobj.write(word + '\t' + handle + '\n')
else:
fiobj.write('\n')
def convertTag():
fiobj = open( home_dir + 'people-daily.txt','r')
trainobj = open( home_dir +'train.data','w' )
testobj = open( home_dir +'test.data','w')
arr = fiobj.readlines()
i = 0
for a in sys.stdin:
i += 1
a = a.strip('\r\n\t ')
if a=="":continue
words = a.split(" ")
test = False
if i % 10 == 0:
test = True
for word in words[1:]:
print "---->", word
word = word.strip('\t ')
if len(word) > 0:
i1 = word.find('[')
if i1 >= 0:
word = word[i1+1:]
i2 = word.find(']')
if i2 > 0:
w = word[:i2]
word_hand = word.split('/')
print "----",word
w,h = word_hand
#print w,h
if h == 'nr': #ren min
#print 'NR',w
if w.find('·') >= 0:
tmpArr = w.split('·')
for tmp in tmpArr:
saveDataFile(trainobj,testobj,test,tmp,h)
continue
saveDataFile(trainobj,testobj,test,w,h)
saveDataFile(trainobj, testobj, test,"","")
trainobj.flush()
testobj.flush()
if __name__ == '__main__':
convertTag()
|
执行训练和测试
设置模板为：
|
1
2
3
4
5
6
7
8
|
# Unigram
U00:%x[-2,0]
U01:%x[-1,0]
U02:%x[0,0]
U03:%x[1,0]
U04:%x[2,0]
U05:%x[-1,0]/%x[0,0]
U06:%x[0,0]/%x[1,0]
|
训练的时候的-p参数根据自己机器情况设置
|
1
2
|
crf_learn -f 3 -p 4 -c 4.0 template train.data model > train.rst
crf_test -m model test.data > test.rst
|
计算准确率
通过命令：python clc_f.py test.rst 执行python脚本，clc_f.py中的具体程序：
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
import sys
if __name__=="__main__":
try:
file = open(sys.argv[1], "r")
except:
print "result file is not specified, or open failed!"
sys.exit()
wc = 0
wc_of_test = 0
wc_of_gold = 0
wc_of_correct = 0
flag = True
for l in file:
if l=='\n': continue
_, g, r = l.strip().split()
if r != g:
flag = False
wc += 1
if flag:
wc_of_correct +=1
flag = True
print "WordCount from result:", wc
print "WordCount of correct post :", wc_of_correct
#准确率
P = wc_of_correct/float(wc)
print "准确率:%f" % (P)
|
实验结果
