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Datawhale 知识图谱小鲸鱼学习 之 Task 4 用户输入->知识库的查询语句

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Datawhale 知识图谱小鲸鱼学习 之 Task 4 用户输入->知识库的查询语句

什么是问答系统?

问答系统简介

问答系统(Question Answering System,QA System)是用来回答人提出的自然语言问题的系统。根据划分标准不同,问答系统可以被分为各种不同的类型。

Query理解

Query理解介绍

Query理解 (QU,Query Understanding),简单来说就是从词法、句法、语义三个层面对 Query 进行结构化解析。

搜索 Query 理解包含的模块主要有:

由于本任务后面代码主要涉及意图识别和槽位解析,因此这里仅对这两部分内容做介绍:

意图识别

介绍:意图识别是用来检测用户当前输入的意图,通常其被建模为将一段自然语言文本分类为预先设定的一个或多个意图的文本分类任务。
所用方法:和文本分类模型的方法大同小异,主要有:

槽值填充

介绍:槽值填充就是根据我们既定的一些结构化字段,将用户输入的信息中与其对应的部分提取出来。因此,槽值填充经常被建模为序列标注的任务。
举例介绍:例如下图所示的 Query “北京飞成都的机票”,通过意图分类模型可以识别出 Query 的整体意图是订机票,在此基础上进一步语义解析出对应的出发地 Depart=“北京”,到达地 Arrive=“成都”,所以生成的形式化表达可以是:Ticket=Order(Depart,Arrive),Depart={北京},Arrive={成都}。

序列标注的任务常用的模型有:

主体类 EntityExtractor 框架介绍

#!/usr/bin/env python3
# coding: utf-8
import os
import ahocorasick
from sklearn.externals import joblib
import jieba
import numpy as np

class EntityExtractor:
    def __init__(self):
        pass

    # 构造actree,加速过滤
    def build_actree(self, wordlist):
        """
        构造actree,加速过滤
        :param wordlist:
        :return:
        """
        pass
    # 模式匹配, 得到匹配的词和类型。如疾病,疾病别名,并发症,症状
    def entity_reg(self, question):
        """
        模式匹配, 得到匹配的词和类型。如疾病,疾病别名,并发症,症状
        :param question:str
        :return:
        """
        pass

    # 当全匹配失败时,就采用相似度计算来找相似的词
    def find_sim_words(self, question):
        """
        当全匹配失败时,就采用相似度计算来找相似的词
        :param question:
        :return:
        """
        pass

    # 采用DP方法计算编辑距离
    def editDistanceDP(self, s1, s2):
        """
        采用DP方法计算编辑距离
        :param s1:
        :param s2:
        :return:
        """
        pass

    # 计算词语和字典中的词的相似度
    def simCal(self, word, entities, flag):
        """
        计算词语和字典中的词的相似度
        相同字符的个数/min(|A|,|B|)   +  余弦相似度
        :param word: str
        :param entities:List
        :return:
        """
        pass

    # 基于特征词分类
    def check_words(self, wds, sent):
        """
        基于特征词分类
        :param wds:
        :param sent:
        :return:
        """
        pass

    # 提取问题的TF-IDF特征
    def tfidf_features(self, text, vectorizer):
        """
        提取问题的TF-IDF特征
        :param text:
        :param vectorizer:
        :return:
        """
        pass

    # 提取问题的关键词特征
    def other_features(self, text):
        """
        提取问题的关键词特征
        :param text:
        :return:
        """
        pass

    # 预测意图
    def model_predict(self, x, model):
        """
        预测意图
        :param x:
        :param model:
        :return:
        """
        pass

    # 实体抽取主函数
    def extractor(self, question):
        pass

命名实体识别任务实践

命名实体识别整体思路介绍

代码介绍

构建 AC Tree

先通过 entity_extractor.py 中 类 EntityExtractor 的 build_actree 函数构建AC Tree

    def build_actree(self, wordlist):
        """
        构造actree,加速过滤
        :param wordlist:
        :return:
        """
        actree = ahocorasick.Automaton()
        # 向树中添加单词
        for index, word in enumerate(wordlist):
            actree.add_word(word, (index, word))
        actree.make_automaton()
        return actree
    def __init__(self):
        ...
        self.disease_path = cur_dir + 'disease_vocab.txt'
        self.symptom_path = cur_dir + 'symptom_vocab.txt'
        self.alias_path = cur_dir + 'alias_vocab.txt'
        self.complication_path = cur_dir + 'complications_vocab.txt'

        self.disease_entities = [w.strip() for w in open(self.disease_path, encoding='utf8') if w.strip()]
        self.symptom_entities = [w.strip() for w in open(self.symptom_path, encoding='utf8') if w.strip()]
        self.alias_entities = [w.strip() for w in open(self.alias_path, encoding='utf8') if w.strip()]
        self.complication_entities = [w.strip() for w in open(self.complication_path, encoding='utf8') if w.strip()]

        self.region_words = list(set(self.disease_entities+self.alias_entities+self.symptom_entities))

        # 构造领域actree
        self.disease_tree = self.build_actree(list(set(self.disease_entities)))
        self.alias_tree = self.build_actree(list(set(self.alias_entities)))
        self.symptom_tree = self.build_actree(list(set(self.symptom_entities)))
        self.complication_tree = self.build_actree(list(set(self.complication_entities)))
        ...

使用AC Tree进行问句过滤

    def entity_reg(self, question):
        """
        模式匹配, 得到匹配的词和类型。如疾病,疾病别名,并发症,症状
        :param question:str
        :return:
        """
        self.result = {}

        for i in self.disease_tree.iter(question):
            word = i[1][1]
            if "Disease" not in self.result:
                self.result["Disease"] = [word]
            else:
                self.result["Disease"].append(word)

        for i in self.alias_tree.iter(question):
            word = i[1][1]
            if "Alias" not in self.result:
                self.result["Alias"] = [word]
            else:
                self.result["Alias"].append(word)

        for i in self.symptom_tree.iter(question):
            wd = i[1][1]
            if "Symptom" not in self.result:
                self.result["Symptom"] = [wd]
            else:
                self.result["Symptom"].append(wd)

        for i in self.complication_tree.iter(question):
            wd = i[1][1]
            if "Complication" not in self.result:
                self.result["Complication"] = [wd]
            else:
                self.result["Complication"] .append(wd)

        return self.result
    def extractor(self, question):
        self.entity_reg(question)
        ...

使用 相似度进行实体匹配

当AC Tree的匹配都没有匹配到实体时,使用查找相似词的方式进行实体匹配

def find_sim_words(self, question):
    """
    当全匹配失败时,就采用相似度计算来找相似的词
    :param question:
    :return:
    """
    import re
    import string
    from gensim.models import KeyedVectors
    
    # 使用结巴加载自定义词典
    jieba.load_userdict(self.vocab_path)
    # 加载词向量
    self.model = KeyedVectors.load_word2vec_format(self.word2vec_path, binary=False)
    
    # 数据预处理,正则去除特殊符号
    sentence = re.sub("[{}]", re.escape(string.punctuation), question)
    sentence = re.sub("[,。‘’;:?、!【】]", " ", sentence)
    sentence = sentence.strip()
    
    # 使用结巴进行分词
    words = [w.strip() for w in jieba.cut(sentence) if w.strip() not in self.stopwords and len(w.strip()) >= 2]

    alist = []
    
    # 对每个词,都让其与每类实体词典进行相似对比,
    # 最终选取分数最高的实体和其属于的实体类型
    for word in words:
        temp = [self.disease_entities, self.alias_entities, self.symptom_entities, self.complication_entities]
        for i in range(len(temp)):
            flag = ''
            if i == 0:
                flag = "Disease"
            elif i == 1:
                flag = "Alias"
            elif i == 2:
                flag = "Symptom"
            else:
                flag = "Complication"
            scores = self.simCal(word, temp[i], flag)
            alist.extend(scores)
    temp1 = sorted(alist, key=lambda k: k[1], reverse=True)
    if temp1:
        self.result[temp1[0][2]] = [temp1[0][0]]

# 计算词语和字典中的词的相似度
def simCal(self, word, entities, flag):
    """
    计算词语和字典中的词的相似度
    相同字符的个数/min(|A|,|B|)   +  余弦相似度
    :param word: str
    :param entities:List
    :return:
    """
    a = len(word)
    scores = []
    for entity in entities:
        sim_num = 0
        b = len(entity)
        c = len(set(entity+word))
        temp = []
        for w in word:
            if w in entity:
                sim_num += 1
        if sim_num != 0:
            score1 = sim_num / c  # overlap score
            temp.append(score1)
        try:
            score2 = self.model.similarity(word, entity)  # 余弦相似度分数
            temp.append(score2)
        except:
            pass
        score3 = 1 - self.editDistanceDP(word, entity) / (a + b)  # 编辑距离分数
        if score3:
            temp.append(score3)

        score = sum(temp) / len(temp)
        if score >= 0.7:
            scores.append((entity, score, flag))

    scores.sort(key=lambda k: k[1], reverse=True)
    return scores

意图识别任务实践

意图识别整体思路介绍

该项目通过手工标记210条意图分类训练数据,并采用朴素贝叶斯算法训练得到意图分类模型。其最佳测试效果的F1值达到了96.68%。

特征构建

TF-IDF特征

# 提取问题的TF-IDF特征
def tfidf_features(self, text, vectorizer):
    """
    提取问题的TF-IDF特征
    :param text:
    :param vectorizer:
    :return:
    """
    jieba.load_userdict(self.vocab_path)
    words = [w.strip() for w in jieba.cut(text) if w.strip() and w.strip() not in self.stopwords]
    sents = [' '.join(words)]

    tfidf = vectorizer.transform(sents).toarray()
    return tfidf

人工特征

self.symptom_qwds = ['什么症状', '哪些症状', '症状有哪些', '症状是什么', '什么表征', '哪些表征', '表征是什么',
                     '什么现象', '哪些现象', '现象有哪些', '症候', '什么表现', '哪些表现', '表现有哪些',
                     '什么行为', '哪些行为', '行为有哪些', '什么状况', '哪些状况', '状况有哪些', '现象是什么',
                     '表现是什么', '行为是什么']  # 询问症状
self.cureway_qwds = ['药', '药品', '用药', '胶囊', '口服液', '炎片', '吃什么药', '用什么药', '怎么办',
                     '买什么药', '怎么治疗', '如何医治', '怎么医治', '怎么治', '怎么医', '如何治',
                     '医治方式', '疗法', '咋治', '咋办', '咋治', '治疗方法']  # 询问治疗方法
self.lasttime_qwds = ['周期', '多久', '多长时间', '多少时间', '几天', '几年', '多少天', '多少小时',
                      '几个小时', '多少年', '多久能好', '痊愈', '康复']  # 询问治疗周期
self.cureprob_qwds = ['多大概率能治好', '多大几率能治好', '治好希望大么', '几率', '几成', '比例',
                      '可能性', '能治', '可治', '可以治', '可以医', '能治好吗', '可以治好吗', '会好吗',
                      '能好吗', '治愈吗']  # 询问治愈率
self.check_qwds = ['检查什么', '检查项目', '哪些检查', '什么检查', '检查哪些', '项目', '检测什么',
                   '哪些检测', '检测哪些', '化验什么', '哪些化验', '化验哪些', '哪些体检', '怎么查找',
                   '如何查找', '怎么检查', '如何检查', '怎么检测', '如何检测']  # 询问检查项目
self.belong_qwds = ['属于什么科', '什么科', '科室', '挂什么', '挂哪个', '哪个科', '哪些科']  # 询问科室
self.disase_qwds = ['什么病', '啥病', '得了什么', '得了哪种', '怎么回事', '咋回事', '回事',
                    '什么情况', '什么问题', '什么毛病', '啥毛病', '哪种病']  # 询问疾病
 
def other_features(self, text):
	"""
	提取问题的关键词特征
	:param text:
	:return:
	"""
	features = [0] * 7
	for d in self.disase_qwds:
	    if d in text:
	        features[0] += 1
	
	for s in self.symptom_qwds:
	    if s in text:
	        features[1] += 1
	
	for c in self.cureway_qwds:
	    if c in text:
	        features[2] += 1
	
	for c in self.check_qwds:
	    if c in text:
	        features[3] += 1
	for p in self.lasttime_qwds:
	    if p in text:
	        features[4] += 1
	
	for r in self.cureprob_qwds:
	    if r in text:
	        features[5] += 1
	
	for d in self.belong_qwds:
	    if d in text:
	        features[6] += 1
	
	m = max(features)
	n = min(features)
	normed_features = []
	if m == n:
	    normed_features = features
	else:
	    for i in features:
	        j = (i - n) / (m - n)
	        normed_features.append(j)
	
	return np.array(normed_features)

使用朴素贝叶斯进行文本分类

项目没有给出训练过程,可参考下面sklearn的例子

    # 项目没有给出训练过程,可参考下面sklearn的例子
    from sklearn.naive_bayes import MultinomialNB 

    mnb = MultinomialNB()   
    mnb.fit(X_train,y_train)   
    y_predict = mnb.predict(X_test)

    # 意图分类模型文件
    self.tfidf_path = os.path.join(cur_dir, 'model/tfidf_model.m')
    self.nb_path = os.path.join(cur_dir, 'model/intent_reg_model.m')  #朴素贝叶斯模型
    self.tfidf_model = joblib.load(self.tfidf_path)
    self.nb_model = joblib.load(self.nb_path)

    # 意图预测
    tfidf_feature = self.tfidf_features(question, self.tfidf_model)

    other_feature = self.other_features(question)
    m = other_feature.shape
    other_feature = np.reshape(other_feature, (1, m[0]))
    feature = np.concatenate((tfidf_feature, other_feature), axis=1)
    predicted = self.model_predict(feature, self.nb_model)
    intentions.append(predicted[0])

根据所识别的实体进行补充和纠正意图

# 已知疾病,查询症状
if self.check_words(self.symptom_qwds, question) and ('Disease' in types or 'Alia' in types):
    intention = "query_symptom"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病或症状,查询治疗方法
if self.check_words(self.cureway_qwds, question) and \
        ('Disease' in types or 'Symptom' in types or 'Alias' in types or 'Complication' in types):
    intention = "query_cureway"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病或症状,查询治疗周期
if self.check_words(self.lasttime_qwds, question) and ('Disease' in types or 'Alia' in types):
    intention = "query_period"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病,查询治愈率
if self.check_words(self.cureprob_qwds, question) and ('Disease' in types or 'Alias' in types):
    intention = "query_rate"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病,查询检查项目
if self.check_words(self.check_qwds, question) and ('Disease' in types or 'Alias' in types):
    intention = "query_checklist"
    if intention not in intentions:
        intentions.append(intention)
# 查询科室
if self.check_words(self.belong_qwds, question) and \
        ('Disease' in types or 'Symptom' in types or 'Alias' in types or 'Complication' in types):
    intention = "query_department"
    if intention not in intentions:
        intentions.append(intention)
# 已知症状,查询疾病
if self.check_words(self.disase_qwds, question) and ("Symptom" in types or "Complication" in types):
    intention = "query_disease"
    if intention not in intentions:
        intentions.append(intention)

# 若没有检测到意图,且已知疾病,则返回疾病的描述
if not intentions and ('Disease' in types or 'Alias' in types):
    intention = "disease_describe"
    if intention not in intentions:
        intentions.append(intention)
# 若是疾病和症状同时出现,且出现了查询疾病的特征词,则意图为查询疾病
if self.check_words(self.disase_qwds, question) and ('Disease' in types or 'Alias' in types) \
        and ("Symptom" in types or "Complication" in types):
    intention = "query_disease"
    if intention not in intentions:
        intentions.append(intention)
# 若没有识别出实体或意图则调用其它方法
if not intentions or not types:
    intention = "QA_matching"
    if intention not in intentions:
        intentions.append(intention)

self.result["intentions"] = intentions

后续就是通过上述得到的意图信息和实体信息选择对应的模版,并将实体信息填充入组成查询语句进行数据库查询。

参考资料

[Datawhale 知识图谱组队学习 之 Task 4 用户输入->知识库的查询语句

QASystemOnMedicalGraph

标签:Task,features,intentions,self,知识库,question,Datawhale,intention,types
来源: https://blog.csdn.net/m0_49727407/article/details/112643978