CRF++地名实体识别（特征为词性和词）

http://x-algo.cn/index.php/2016/02/29/crf-name-entity-recognition/

类似使用CRF实现分词和词性标注，地域识别也是需要生成相应的tag进行标注。这里使用的语料库是1998年1月人民日报语料集。最终学习出来的模型，对复杂的地名识别准确率（F值）非常低，推测是预料中对地名的标注多处是前后矛盾。例如 [华南/ns 地区/n]ns 标为地名实体，但是 东北/f 地区/n 确分开标注，类似错误还有很多。将来有时间可以考虑使用微软的词库戳我下载-微软词库。

本文还是在人民日报的语料之下，在分完词的粒度BMES标注最后效果如下：

------ LOC_E -------

[LOC_E] P = 0.832215, R = 0.629442, F-score = 0.716763

------ LOC_B -------

[LOC_B] P = 0.781022, R = 0.543147, F-score = 0.640719

------ LOC_S -------

[LOC_S] P = 0.986800, R = 0.994489, F-score = 0.990629

------ LOC_I -------

[LOC_I] P = 0.736842, R = 0.442105, F-score = 0.552632

------ All -------

[All] P = 0.975204, R = 0.957399, F-score = 0.966219

由于单字识别F值很高，并且数量多，所以整个识别的效果还是很高。语料、相关代码下载：[戳我下载]crf++地名实体识别，下面为具体流程。

文章目录 [展开]

生成训练和测试数据

通过一个python脚本按照一定比例生成训练和测试数据，生成过程中按照BMES对语料进行标识，具体规则如下：

通过调用脚本： cat people-daily.txt | python get_ner_loc_train_test_data.py >log 生成所需要的训练和测试数据，中间过程打印出来很多调试信息，打印到标准输出话费较多时间。具体代码如下（已折叠）：

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#coding=utf8

import sys

home_dir = "./"

def saveDataFile(trainobj,testobj,isTest,word,handle,tag):

if isTest:

saveTrainFile(testobj,word,handle,tag)

else:

saveTrainFile(trainobj,word,handle,tag)

def saveTrainFile(fiobj,word,handle,tag):

if len(word) > 0 and word != "。" and word != "，":

fiobj.write(word + '\t' + handle + '\t' +tag +'\n')

else:

fiobj.write('\n')

#填充地点标注，非地点的不添加

def fill_local_tag(words, tags):

pos = 0

while True:

print "pos:", pos, " len:", len(words)

if pos == len(words):

print "添加地点tag执行结束"

print tags

break

word = words[pos]

left = word.find("[")

if left == -1 :

print "单个词", word

w,h = word.split("/")

print w,h

if h == "ns": #单个词是地点

tags[pos] = "LOC_S"

print "本轮tag",tags[pos]

pos += 1

elif left >= 0:

print "发现词组" ,word

search_pos = pos

for word in words[pos+1:]:

print word

search_pos += 1

if word.find("[") >=0:

print "括号配对异常"

sys.exit(255)

if word.find("]") >=0:

break

if words[search_pos].find("]") == -1:

print "括号配对异常，搜索到句尾没有找都另一半括号"

sys.exit(255)

else:

#找到另一半，判断原始标注是不是ns，如果是就进行tag标注

print "match到一个组", words[pos:search_pos+1]

h = words[search_pos].split("]")[-1] #最后一个词性

if h == "ns":

tags[pos] = "LOC_B" #添加首个词

for p in range(pos + 1,search_pos + 1):

tags[p] = "LOC_I" #中间词

tags[search_pos] = "LOC_E" #找到最后一个词

else:

p = pos

for word in words[pos:search_pos+1]:

print "hhhhhhh", word

w,h = word.strip("[").split("]")[0].split("/")

if h == "ns":

tags[p] = "LOC_S"

p += 1

#移动pos

print "本轮添加的tag", tags[pos:search_pos+1]

pos = search_pos + 1

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 % 5 == 0:

test = True

words = words[1:]

if len(words) == 0: continue

tags = ["O"] * len(words)

fill_local_tag(words, tags)

pos = -1

for word in words:

pos += 1

print "---->", word

word = word.strip('\t ')

if len(word) == 0:

print "Warning 发现空词"

continue

l1 = word.find('[')

if l1 >=0:

word = word[l1+1:]

l2 = word.find(']')

if l2 >= 0:

word = word[:l2]

w,h = word.split('/')

saveDataFile(trainobj,testobj,test,w,h,tags[pos])

saveDataFile(trainobj, testobj, test,"","","")

trainobj.flush()

testobj.flush()

if __name__ == '__main__':

convertTag()

模板文件

#Unigram

U01:%x[-1,0]

U02:%x[0,0]

U03:%x[1,0]

U04:%x[2,0]

U05:%x[-2,1]

U06:%x[-1,1]

U07:%x[0,1]

U08:%x[1,1]

U09:%x[2,1]

U0:%x[-2,0]

U10:%x[0,0]/%x[0,1]

U11:%x[-2,1]%x[-1,1]

U18:%x[0,0]/%x[-1,0]

U12:%x[0,0]%x[1,0]

U13:%x[0,1]%x[-1,0]

U14:%x[0,0]%x[1,1]

U15:%x[-1,0]%x[-1,1]

U16:%x[-1,0]%x[-2,0]

U17:%x[-2,0]%x[-2,1]

U18:%x[1,0]%x[2,0]

U19:%x[-1,0]%x[1,0]

U20:%x[1,0]%x[0,1]

U22:%x[-2,1]%x[0,1]

U23:%x[-1,1]%x[0,1]

U24:%x[-1,1]%x[1,1]

U25:%x[0,1]%x[1,1]

U26:%x[0,1]%x[2,1]

U27:%x[1,1]%x[2,1]

开始训练和测试

通过下面命令执行训练和测试过程：

1 2	crf_learn -f 4 -p 4 -c 3 template train.data model > train.rst crf_test -m model test.data > test.rst

分类型计算F值

通过执行： python clc.py test.rst 执行脚本，脚本内容如下：

#!/usr/bin/python

# -*- coding: utf-8 -*-

import sys

god_dic={"LOC_S":0,"LOC_B":0, "LOC_I":0, "LOC_E":0}

pre_dic={"LOC_S":0,"LOC_B":0, "LOC_I":0, "LOC_E":0}

correct_dic={"LOC_S":0,"LOC_B":0, "LOC_I":0, "LOC_E":0}

if __name__=="__main__":

try:

file = open(sys.argv[1], "r")

except:

print "result file is not specified, or open failed!"

sys.exit()

wc = 0

loc_wc = 0

wc_of_test = 0

wc_of_gold = 0

wc_of_correct = 0

flag = True

for l in file:

wc += 1

if l=='\n': continue

_,_, g, r = l.strip().split()

#并不涉及到地点实体识别

if "LOC" not in g and "LOC" not in r: continue

loc_wc += 1

if "LOC" in g:

god_dic[g]+= 1

if "LOC" in r:

pre_dic[r]+=1

if g == r:

correct_dic[r]+=1

print "WordCount from result:", wc

print "WordCount of loc_wc post :", loc_wc

print "真实位置标记个数：", god_dic

print "预估位置标记个数：",pre_dic

print "正确标记个数：", correct_dic

res ={"LOC_S":0.0,"LOC_B":0.0, "LOC_I":0.0, "LOC_E":0.0}

all_gold = 0

all_correct = 0

all_pre = 0

for k in god_dic:

print "------ %s -------"%(k)

R = correct_dic[k]/float(god_dic[k])

P = correct_dic[k]/float(pre_dic[k])

print "[%s] P = %f, R = %f, F-score = %f" % (k,P, R, (2*P*R)/(P+R))

all_pre += pre_dic[k]

all_correct += correct_dic[k]

all_gold += god_dic[k]

print "------ All -------"

all_R = all_correct/float(all_gold)

all_P = all_correct/float(all_pre)

print "[%s] P = %f, R = %f, F-score = %f" % ("All",all_P, all_R, (2*all_P*all_R)/(all_P+all_R))

参考文献

基于 CRF和规则相结合的地理命名实体识别方法何炎祥1,2 罗楚威2 胡彬尧