CRF随机条件场进行中文文本分词.zip

import torch count = 0 class myCRF: def __init__(self): self.scoreMap = {} #分数表 self.UnigramTemplates = [] #状态特征模板 self.BigramTemplates = [] #转移特征模板 self.readTemplate() #读取特征模板 def readTemplate(self,debug=False): ''' 读取特征模板 :return: ''' tempFile = open("../dataset/dataset2/template.utf8", encoding='utf-8') switchFlag = False # 先读Unigram，在读Bigram for line in tempFile: tmpList = [] if line.find("Unigram") > 0 or line.find("Bigram") > 0: # 读到'Unigram'或者'Bigram' continue if switchFlag: if line.find("/") > 0: left = line.split("/")[0].split("[")[-1].split(",")[0] right = line.split("/")[-1].split("[")[-1].split(",")[0] tmpList.append(int(left)) tmpList.append(int(right)) else: num = line.split("[")[-1].split(",")[0] tmpList.append(int(num)) self.BigramTemplates.append(tmpList) else: if len(line.strip()) == 0: switchFlag = True else: if line.find("/") > 0: left = line.split("/")[0].split("[")[-1].split(",")[0] right = line.split("/")[-1].split("[")[-1].split(",")[0] tmpList.append(int(left)) tmpList.append(int(right)) else: num = line.split("[")[-1].split(",")[0] tmpList.append(int(num)) self.UnigramTemplates.append(tmpList) if (debug == True): print(self.UnigramTemplates) print(self.BigramTemplates) def getTrainData(self): sentences = [] results = [] tempFile = open('../dataset/dataset2/train.utf8', encoding='utf-8') sentence = "" result = "" for line in tempFile: line = line.strip() if line == "": if sentence == "" or result == "": pass else: sentences.append(sentence) results.append(result) sentence = "" result = "" else: data = line.split(" ") sentence += data[0] result += data[1] return [sentences, results] def getUnigramScore(self, sentence, thisPos, thisStatus): ''' 获得给定词和标志的状态特征分数和 :param sentence: 句子 :param thisPos: 当前位置 :param thisStatus: 当前标志 :return: 得分 ''' unigramScore = 0 unigramTemplates = self.UnigramTemplates for i in range(0, len(unigramTemplates)): key = self.makeKey(unigramTemplates[i], str(i), sentence, thisPos, thisStatus) if key in self.scoreMap: #这里为了加快运算，将对应的一组分数相加求和 unigramScore += self.scoreMap[key] return unigramScore def getBigramScore(self, sentence, thisPos, preStatus, thisStatus): ''' 获得给定词和标志的转移特征分数和 :param sentence: 句子 :param thisPos: 当前位置 :param preStatus: 上一个特征 :param thisStatus: 当前特征 :return: 得分 ''' bigramScore = 0 bigramTemplates = self.BigramTemplates for i in range(0, len(bigramTemplates)): key = self.makeKey(bigramTemplates[i], str(i), sentence, thisPos, preStatus + thisStatus) if key in self.scoreMap: bigramScore += self.scoreMap[key] return bigramScore def num2Tag(self, number): ''' 将数字转为对应标志 :param number: 数字 :return: 标志 ''' if number == 0: return "B" elif number == 1: return "I" elif number == 2: return "E" elif number == 3: return "S" else: return None def tag2Num(self, status): ''' 将标志转为对应数字 :param status: 标志 :return: 数字 ''' if status == "B": return 0 elif status == "I": return 1 elif status == "E": return 2 elif status == "S": return 3 else: return -1 def getMaxIndex(self, list): ''' 获取最大值所在列表的序号 :param list: 列表 :return: 最大值的序号 ''' origin = list.copy() origin.sort() max = origin[-1] index = list.index(max) return index def getDuplicate(self, realstring, string): ''' 状态序列里，正确的状态的个数 :param realstring: 正确解 :param string: 输出解 :return: 正确个数 ''' length = min(len(realstring), len(string)) count = 0 for i in range(0, length): if realstring[i] == string[i]: count += 1 return count def makeKey(self, template, identity, sentence, pos, statusCovered,debug=False): ''' 找出一句句子中，给定的模板下的某位置的标注（BIES） :param template: 给定模板 :param identity: 模板序号 :param sentence: 标注句子 :param pos: 当点位置 :param statusCovered: 状态标注 :param debug: 调试用 :return: 标注结果 ''' result = "" result += identity for i in template: index = pos + i if index < 0 or index >= len(sentence): result += " " else: result += sentence[index] result += "/" result += statusCovered if (debug==True): print(result) return result def getWrongNum(self, sentence, realRes): ''' 计算正确率 :param sentence: 句子 :param realRes: 正确解 :return: 错误个数 ''' myRes = self.Viterbi(sentence) # 我的解 lens = len(sentence) wrongNum = 0 for i in range(0, lens): myResI = myRes[i] # 我的解 realResI = realRes[i] # 理论解 if myResI != realResI: wrongNum += 1 return wrongNum def setScoreMap(self, sentence, realRes,debug =False): ''' 建立状态特征和转移特征的特征矩阵，并依据结果为每个元素打分 :param sentence: 句子 :param realRes: 正确解 :param debug: 调试用 :return: ''' myRes = self.Viterbi(sentence) # 我的解 for word in range(0, len(sentence)): myResI = myRes[word] # 我的解 realResI = realRes[word] # 理论解 if myResI != realResI: # 如果和理论值不同 # print("Unigram更新开始") uniTem = self.UnigramTemplates for uniIndex in range(0, len(uniTem)): if debug == True: print(uniTem[uniIndex]) print(str(uniIndex)) print(sentence) print(myResI) uniMyKey = self.makeKey(uniTem[uniIndex], str(uniIndex), sentence, word, myResI) # 我的状态