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 。
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：
| 1 2 3 4 5 6 7 8 9 10 | ------ 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：，下面为具体流程。
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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）：
| 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 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | #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
| 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 | #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：
| 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 | #!/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
