NLP-文本处理：语义角色标注（触发者、谓语、受事者、时间..）【基于“句子A的分词列表B”+“B的词性列表C”+“B与C的依存句法分析结果”】--＞关系抽取--＞知识图谱

柔光的暖阳◎ 2023-10-05 16:56 23阅读 0赞

## 一、LTP进行语义角色标注 ##

import os
    from pyltp import Segmentor, Postagger, Parser, NamedEntityRecognizer, SementicRoleLabeller
    # pip install pyltp -i https://pypi.tuna.tsinghua.edu.cn/simple 可以先下载好whl文件
    #LTP语言平台：http://ltp.ai/index.html
    #咱们使用的工具包,pyltp:https://pyltp.readthedocs.io/zh_CN/latest/api.html
    #LTP附录：https://ltp.readthedocs.io/zh_CN/latest/appendix.html#id3
    #安装方法：https://github.com/HIT-SCIR/pyltp
    class LtpParser:
        def __init__(self):
            LTP_DIR = "./ltp_data_v3.4.0"
            self.segmentor = Segmentor()    # 分词
            self.segmentor.load(os.path.join(LTP_DIR, "cws.model"))
    
            self.postagger = Postagger()    # 词性标注
            self.postagger.load(os.path.join(LTP_DIR, "pos.model"))
    
            self.parser = Parser()  # 句法依存分析
            self.parser.load(os.path.join(LTP_DIR, "parser.model"))
    
            self.recognizer = NamedEntityRecognizer()   # 命名实体识别
            self.recognizer.load(os.path.join(LTP_DIR, "ner.model"))
    
            self.labeller = SementicRoleLabeller()  # 语义角色标注
            self.labeller.load(os.path.join(LTP_DIR, 'pisrl_win.model'))
    
        # 语义角色标注
        def format_labelrole(self, words, postags):
            print("分词----> words= {0}----len(words) = {1}".format(words, len(words)))
            print("词性标注----> postags= {0}----len(postags) = {1}".format(postags, len(postags)))
            arcs = self.parser.parse(words, postags)  # 建立依存句法分析树
            roles = self.labeller.label(words, postags, arcs)
            print("len(roles) = {0}----roles = {1}".format(len(roles), roles))
            roles_dict = {
        }
            for role in roles:
                print("谓语所在索引：role.index = {0}".format(role.index))
                roles_dict[role.index] = {
        arg.name:[arg.name,arg.range.start, arg.range.end] for arg in role.arguments}
            # {6: {'A0': ['A0', 0, 2], 'TMP': ['TMP', 3, 3], 'LOC': ['LOC', 4, 5], 'A1': ['A1', 8, 8]}}
            # 6：表示谓语（发表）所在序号；
            # A0：表示“施事者、主体、触发者”，0,2分别表示A0所在的起始索引、终止索引（此句中有2个A0，分别是“奥巴马”、“克林顿”，索引范围是是0-2）
            # TMP：表示“时间”，3, 3分别表示TMP所在的起始索引、终止索引（“昨晚”）
            # LOC：表示“地点”，4, 5分别表示LOC所在的起始索引、终止索引（“在”,“白宫”）
            # A1：表示“受事者”，8, 8分别表示LOC所在的起始索引、终止索引（“演说”）
            print("语义角色标注---->roles_dict = {0}".format(roles_dict))
            return roles_dict
    
        '''parser主函数'''
        def parser_main(self, sentence):
            # 分词
            words = list(self.segmentor.segment(sentence))
            # 词性标注
            postags = list(self.postagger.postag(words))
            # 语义角色标注
            roles_dict = self.format_labelrole(words, postags)
    
            return words, postags, roles_dict
    
    
    if __name__ == '__main__':
        parse = LtpParser()
        sentence = '奥巴马与克林顿昨晚在白宫发表了演说'
        words, postags, roles_dict = parse.parser_main(sentence)

输出结果：

分词----> words= ['奥巴马', '与', '克林顿', '昨晚', '在', '白宫', '发表', '了', '演说']----len(words) = 9
    词性标注----> postags= ['nh', 'p', 'nh', 'nt', 'p', 'n', 'v', 'u', 'v']----len(postags) = 9
    len(roles) = 1----roles = <pyltp.SementicRoles object at 0x000002170D3CC210>
    谓语所在索引：role.index = 6
    语义角色标注---->roles_dict = {
        6: {
        'A0': ['A0', 0, 2], 'TMP': ['TMP', 3, 3], 'LOC': ['LOC', 4, 5], 'A1': ['A1', 8, 8]}}
    
    Process finished with exit code 0

## 二、“语义角色标注”结果分析： ##

`{6: {'A0': ['A0', 0, 2], 'TMP': ['TMP', 3, 3], 'LOC': ['LOC', 4, 5], 'A1': ['A1', 8, 8]}}`

*  6：表示谓语（发表）所在序号；
 *  A0：表示“施事者、主体、触发者”，0,2分别表示A0所在的起始索引、终止索引（此句中有2个A0，分别是“奥巴马”、“克林顿”，索引范围是是0-2）
 *  TMP：表示“时间”，3, 3分别表示TMP所在的起始索引、终止索引（“昨晚”）
 *  LOC：表示“地点”，4, 5分别表示LOC所在的起始索引、终止索引（“在”,“白宫”）
 *  A1：表示“受事者”，8, 8分别表示LOC所在的起始索引、终止索引（“演说”）

## 三、关系抽取【三元组（Triple、主谓宾） 抽取】 ##

import os
    import re
    from pyltp import Segmentor, Postagger, Parser, NamedEntityRecognizer, SementicRoleLabeller
    # pip install pyltp -i https://pypi.tuna.tsinghua.edu.cn/simple 可以先下载好whl文件
    #LTP语言平台：http://ltp.ai/index.html
    #咱们使用的工具包,pyltp:https://pyltp.readthedocs.io/zh_CN/latest/api.html
    #LTP附录：https://ltp.readthedocs.io/zh_CN/latest/appendix.html#id3
    #安装方法：https://github.com/HIT-SCIR/pyltp
    
    
    class LtpParser:
        def __init__(self):
            LTP_DIR = "./ltp_data_v3.4.0"
            self.segmentor = Segmentor()    # 分词
            self.segmentor.load(os.path.join(LTP_DIR, "cws.model"))
    
            self.postagger = Postagger()    # 词性标注
            self.postagger.load(os.path.join(LTP_DIR, "pos.model"))
    
            self.parser = Parser()  # 句法依存分析
            self.parser.load(os.path.join(LTP_DIR, "parser.model"))
    
            self.recognizer = NamedEntityRecognizer()   # 命名实体识别
            self.recognizer.load(os.path.join(LTP_DIR, "ner.model"))
    
            self.labeller = SementicRoleLabeller()  # 语义角色标注
            self.labeller.load(os.path.join(LTP_DIR, 'pisrl_win.model'))
    
        # 依存句法分析【为句子中的每个词语维护一个保存句法依存儿子节点的字典】
        def build_parse_child_dict(self, words, postags): # words：分词后的结果；postags：词性标注后的结果；arcs：依存句法分析树
            print("-" * 50, "依存句法分析：开始", "-" * 50)
            child_dict_list = []
            format_parse_list = []
            arcs = self.parser.parse(words, postags)  # 建立依存句法分析树
            print("分词列表：words = {}".format(words))
            print("词性分析：postags = {}".format(postags))
            rely_ids = [arc.head - 1 for arc in arcs]  # 提取该句话的每一个词的依存父节点id【0为ROOT，词语从1开始编号】: [2, 0, 2, 5, 8, 8, 6, 3] - 1 =  [1, -1, 1, 4, 7, 7, 5, 2]【此时 -1 表示ROOT】
            print("各个词语所依赖的父节点：rely_ids = {0}".format(rely_ids))
            heads = ['Root' if rely_id == -1 else words[rely_id] for rely_id in rely_ids]  # 匹配依存父节点词语
            print("各个词语所依赖的父节点词语 = {0}".format(heads))
            relations = [arc.relation for arc in arcs]  # 提取依存关系
            print("各个词语与所依赖的父节点的依赖关系 = {0}".format(relations))
    
            for word_index in range(len(words)):
                print("\nword_index = {0}----word = {1}".format(word_index, words[word_index]))
                child_dict = dict() # 每个词语与所有其他词语的关系字典
                for arc_index in range(len(arcs)):  # arc_index==0时表示ROOT【还没进入“我想听一首迪哥的歌”语句】，arc_index==1时表示“我”
                    # 当“依存句法分析树”遍历，遇到当前词语时，说明当前词语在依存句法分析树中与其他词语有依存关系
                    if word_index == rely_ids[arc_index]:  # arcs[arc_index].head 表示arcs[arc_index]所代表的词语依存弧的父结点的索引。 ROOT 节点的索引是 0 ，第一个词开始的索引依次为1，2，3，···【“我”的索引为1】arc. relation 表示依存弧的关系。
                        print("word_index = {0}----arc_index = {1}----rely_ids[arc_index] = {2}----relations[arc_index] = {3}".format(word_index, arc_index, rely_ids[arc_index], relations[arc_index]))
                        if relations[arc_index] in child_dict:  # arcs[arc_index].relation表示arcs[arc_index]所代表的词语与父节点的依存关系(语法关系)
                            child_dict[relations[arc_index]].append(arc_index) # 添加 child_dict = {'ATT': [4]}----> child_dict = {'ATT': [4, 5]}
                        else:
                            child_dict[relations[arc_index]] = [] # 新建
                            child_dict[relations[arc_index]].append(arc_index)  # child_dict = {[]}----> child_dict = {'ATT': [4]}
                        print("child_dict = {0}".format(child_dict))
                child_dict_list.append(child_dict)# 每个词对应的依存关系父节点和其关系
                print("child_dict_list = {0}".format(child_dict_list))
            # 整合每个词语的句法依存关系
            for i in range(len(words)):
                a = [relations[i], words[i], i, postags[i], heads[i], rely_ids[i]-1, postags[rely_ids[i]-1]]
                format_parse_list.append(a)
            print("整合每个词语的句法依存关系---->format_parse_list = ", format_parse_list)
            print("-" * 50, "依存句法分析：结束", "-" * 50)
            return child_dict_list, format_parse_list
    
        # 语义角色标注
        def format_labelrole(self, words, postags):
            print("-"*50, "语义角色标注：开始", "-"*50)
            print("分词----> words= {0}----len(words) = {1}".format(words, len(words)))
            print("词性标注----> postags= {0}----len(postags) = {1}".format(postags, len(postags)))
            arcs = self.parser.parse(words, postags)  # 建立依存句法分析树
            roles = self.labeller.label(words, postags, arcs)
            print("len(roles) = {0}----roles = {1}".format(len(roles), roles))
            roles_dict = {
        }
            for role in roles:
                print("谓语所在索引：role.index = {0}".format(role.index))
                roles_dict[role.index] = {
        arg.name:[arg.name,arg.range.start, arg.range.end] for arg in role.arguments}
            # {6: {'A0': ['A0', 0, 2], 'TMP': ['TMP', 3, 3], 'LOC': ['LOC', 4, 5], 'A1': ['A1', 8, 8]}}
            # 6：表示谓语（发表）所在序号；
            # A0：表示“施事者、主体、触发者”，0,2分别表示A0所在的起始索引、终止索引（此句中有2个A0，分别是“奥巴马”、“克林顿”，索引范围是是0-2）
            # TMP：表示“时间”，3, 3分别表示TMP所在的起始索引、终止索引（“昨晚”）
            # LOC：表示“地点”，4, 5分别表示LOC所在的起始索引、终止索引（“在”,“白宫”）
            # A1：表示“受事者”，8, 8分别表示LOC所在的起始索引、终止索引（“演说”）
            print("语义角色标注---->roles_dict = {0}".format(roles_dict))
            print("-" * 50, "语义角色标注：结束", "-" * 50)
            return roles_dict
    
        # parser主函数
        def parser_main(self, sentence):
            # 分词
            words = list(self.segmentor.segment(sentence))
            # 词性标注
            postags = list(self.postagger.postag(words))
            # 依存句法分析
            child_dict_list, format_parse_list = self.build_parse_child_dict(words, postags)
            # 语义角色标注
            roles_dict = self.format_labelrole(words, postags)
    
            return words, postags, child_dict_list, format_parse_list, roles_dict
    
    # 关系抽取类
    class TripleExtractor:
        def __init__(self):
            self.parser = LtpParser()
    
        '''文章分句处理, 切分长句，冒号，分号，感叹号等做切分标识'''
        def split_sents(self, content):
            return [sentence for sentence in re.split(r'[？?！!。；;：:\n\r]', content) if sentence]
    
        '''利用语义角色标注,直接获取主谓宾三元组,基于A0,A1,A2'''
        def ruler1(self, words, postags, roles_dict, role_index):
            v = words[role_index]
            role_info = roles_dict[role_index]
            if 'A0' in role_info.keys() and 'A1' in role_info.keys():
                s = ''.join([words[word_index] for word_index in range(role_info['A0'][1], role_info['A0'][2]+1) if
                             postags[word_index][0] not in ['w', 'u', 'x'] and words[word_index]])
                o = ''.join([words[word_index] for word_index in range(role_info['A1'][1], role_info['A1'][2]+1) if
                             postags[word_index][0] not in ['w', 'u', 'x'] and words[word_index]])
                if s  and o:
                    return '1', [s, v, o]
            return '4', []
    
        '''三元组抽取主函数'''
        def ruler2(self, words, postags, child_dict_list, format_parse_list, roles_dict):
            svos = []
            for index in range(len(postags)):
                tmp = 1
                # 先借助语义角色标注的结果，进行三元组抽取
                if index in roles_dict:
                    flag, triple = self.ruler1(words, postags, roles_dict, index)
                    if flag == '1':
                        svos.append(triple)
                        tmp = 0
                if tmp == 1:
                    # 如果语义角色标记为空，则使用依存句法进行抽取
                    # if postags[index] == 'v':
                    if postags[index]:
                    # 抽取以谓词为中心的事实三元组
                        child_dict = child_dict_list[index]
                        # 主谓宾
                        if 'SBV' in child_dict and 'VOB' in child_dict:
                            r = words[index]
                            e1 = self.complete_e(words, postags, child_dict_list, child_dict['SBV'][0])
                            e2 = self.complete_e(words, postags, child_dict_list, child_dict['VOB'][0])
                            svos.append([e1, r, e2])
    
                        # 定语后置，动宾关系
                        relation = format_parse_list[index][0]
                        head = format_parse_list[index][2]
                        if relation == 'ATT':
                            if 'VOB' in child_dict:
                                e1 = self.complete_e(words, postags, child_dict_list, head - 1)
                                r = words[index]
                                e2 = self.complete_e(words, postags, child_dict_list, child_dict['VOB'][0])
                                temp_string = r + e2
                                if temp_string == e1[:len(temp_string)]:
                                    e1 = e1[len(temp_string):]
                                if temp_string not in e1:
                                    svos.append([e1, r, e2])
                        # 含有介宾关系的主谓动补关系
                        if 'SBV' in child_dict and 'CMP' in child_dict:
                            e1 = self.complete_e(words, postags, child_dict_list, child_dict['SBV'][0])
                            cmp_index = child_dict['CMP'][0]
                            r = words[index] + words[cmp_index]
                            if 'POB' in child_dict_list[cmp_index]:
                                e2 = self.complete_e(words, postags, child_dict_list, child_dict_list[cmp_index]['POB'][0])
                                svos.append([e1, r, e2])
            return svos
    
        '''对找出的主语或者宾语进行扩展：【定中关系  ATT 红苹果 (红 <– 苹果)】'''
        def complete_e(self, words, postags, child_dict_list, word_index):
            child_dict = child_dict_list[word_index]
            prefix = ''
            if 'ATT' in child_dict:
                for i in range(len(child_dict['ATT'])):
                    prefix += self.complete_e(words, postags, child_dict_list, child_dict['ATT'][i])
            postfix = ''
            if postags[word_index] == 'v':
                if 'VOB' in child_dict:
                    postfix += self.complete_e(words, postags, child_dict_list, child_dict['VOB'][0])
                if 'SBV' in child_dict:
                    prefix = self.complete_e(words, postags, child_dict_list, child_dict['SBV'][0]) + prefix
    
            return prefix + words[word_index] + postfix
    
    
        '''程序主控函数'''
        def triples_main(self, text):
            sentences = self.split_sents(text)
            svos = []
            for index, sentence in enumerate(sentences):
                print("="*50, "第{}句：开始".format(index + 1), "="*50)
                # words: 分词; postags: 词性标注; child_dict_list: 依存句法分析; roles_dict: 语义角色标注
                words, postags, child_dict_list, format_parse_list, roles_dict = self.parser.parser_main(sentence)
                svo = self.ruler2(words, postags, child_dict_list, format_parse_list, roles_dict)
                print("svo = {0}".format(svo))
                print("=" * 50, "第{}句：结束".format(index + 1), "=" * 50)
                svos += svo
            return svos
    
    # 关系抽取
    def run_extractor(text):
        extractor = TripleExtractor()
        svos = extractor.triples_main(text)
        return svos
    
    if __name__ == '__main__':
        # 关系抽取
        # text =  '奥巴马与克林顿昨晚在白宫发表了演说'
        text = '我购买了一件玩具，孩子非常喜欢这个玩具，但是质量不太好。希望商家能够保障商品质量，不要再出现类似问题。'
        svos = run_extractor(text)
        print("关系抽取结果：svos = {0}".format(svos))

打印结果：

================================================== 第1句：开始 ==================================================
    -------------------------------------------------- 依存句法分析：开始 --------------------------------------------------
    分词列表：words = ['我', '购买', '了', '一', '件', '玩具', '，', '孩子', '非常', '喜欢', '这个', '玩具', '，', '但是', '质量', '不', '太', '好']
    词性分析：postags = ['r', 'v', 'u', 'm', 'q', 'n', 'wp', 'n', 'd', 'v', 'r', 'n', 'wp', 'c', 'n', 'd', 'd', 'a']
    各个词语所依赖的父节点：rely_ids = [1, -1, 1, 4, 5, 1, 1, 9, 9, 1, 11, 9, 9, 17, 17, 17, 17, 9]
    各个词语所依赖的父节点词语 = ['购买', 'Root', '购买', '件', '玩具', '购买', '购买', '喜欢', '喜欢', '购买', '玩具', '喜欢', '喜欢', '好', '好', '好', '好', '喜欢']
    各个词语与所依赖的父节点的依赖关系 = ['SBV', 'HED', 'RAD', 'ATT', 'ATT', 'VOB', 'WP', 'SBV', 'ADV', 'COO', 'ATT', 'VOB', 'WP', 'ADV', 'SBV', 'ADV', 'ADV', 'COO']
    
    word_index = 0----word = 我
    child_dict_list = [{
        }]
    
    word_index = 1----word = 购买
    word_index = 1----arc_index = 0----rely_ids[arc_index] = 1----relations[arc_index] = SBV
    child_dict = {
        'SBV': [0]}
    word_index = 1----arc_index = 2----rely_ids[arc_index] = 1----relations[arc_index] = RAD
    child_dict = {
        'SBV': [0], 'RAD': [2]}
    word_index = 1----arc_index = 5----rely_ids[arc_index] = 1----relations[arc_index] = VOB
    child_dict = {
        'SBV': [0], 'RAD': [2], 'VOB': [5]}
    word_index = 1----arc_index = 6----rely_ids[arc_index] = 1----relations[arc_index] = WP
    child_dict = {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6]}
    word_index = 1----arc_index = 9----rely_ids[arc_index] = 1----relations[arc_index] = COO
    child_dict = {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}]
    
    word_index = 2----word = 了
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }]
    
    word_index = 3----word = 一
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }]
    
    word_index = 4----word = 件
    word_index = 4----arc_index = 3----rely_ids[arc_index] = 4----relations[arc_index] = ATT
    child_dict = {
        'ATT': [3]}
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}]
    
    word_index = 5----word = 玩具
    word_index = 5----arc_index = 4----rely_ids[arc_index] = 5----relations[arc_index] = ATT
    child_dict = {
        'ATT': [4]}
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}]
    
    word_index = 6----word = ，
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }]
    
    word_index = 7----word = 孩子
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }]
    
    word_index = 8----word = 非常
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }]
    
    word_index = 9----word = 喜欢
    word_index = 9----arc_index = 7----rely_ids[arc_index] = 9----relations[arc_index] = SBV
    child_dict = {
        'SBV': [7]}
    word_index = 9----arc_index = 8----rely_ids[arc_index] = 9----relations[arc_index] = ADV
    child_dict = {
        'SBV': [7], 'ADV': [8]}
    word_index = 9----arc_index = 11----rely_ids[arc_index] = 9----relations[arc_index] = VOB
    child_dict = {
        'SBV': [7], 'ADV': [8], 'VOB': [11]}
    word_index = 9----arc_index = 12----rely_ids[arc_index] = 9----relations[arc_index] = WP
    child_dict = {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12]}
    word_index = 9----arc_index = 17----rely_ids[arc_index] = 9----relations[arc_index] = COO
    child_dict = {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}]
    
    word_index = 10----word = 这个
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }]
    
    word_index = 11----word = 玩具
    word_index = 11----arc_index = 10----rely_ids[arc_index] = 11----relations[arc_index] = ATT
    child_dict = {
        'ATT': [10]}
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }, {
        'ATT': [10]}]
    
    word_index = 12----word = ，
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }, {
        'ATT': [10]}, {
        }]
    
    word_index = 13----word = 但是
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }, {
        'ATT': [10]}, {
        }, {
        }]
    
    word_index = 14----word = 质量
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }, {
        'ATT': [10]}, {
        }, {
        }, {
        }]
    
    word_index = 15----word = 不
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }, {
        'ATT': [10]}, {
        }, {
        }, {
        }, {
        }]
    
    word_index = 16----word = 太
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }, {
        'ATT': [10]}, {
        }, {
        }, {
        }, {
        }, {
        }]
    
    word_index = 17----word = 好
    word_index = 17----arc_index = 13----rely_ids[arc_index] = 17----relations[arc_index] = ADV
    child_dict = {
        'ADV': [13]}
    word_index = 17----arc_index = 14----rely_ids[arc_index] = 17----relations[arc_index] = SBV
    child_dict = {
        'ADV': [13], 'SBV': [14]}
    word_index = 17----arc_index = 15----rely_ids[arc_index] = 17----relations[arc_index] = ADV
    child_dict = {
        'ADV': [13, 15], 'SBV': [14]}
    word_index = 17----arc_index = 16----rely_ids[arc_index] = 17----relations[arc_index] = ADV
    child_dict = {
        'ADV': [13, 15, 16], 'SBV': [14]}
    child_dict_list = [{
        }, {
        'SBV': [0], 'RAD': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        }, {
        'ATT': [3]}, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'SBV': [7], 'ADV': [8], 'VOB': [11], 'WP': [12], 'COO': [17]}, {
        }, {
        'ATT': [10]}, {
        }, {
        }, {
        }, {
        }, {
        }, {
        'ADV': [13, 15, 16], 'SBV': [14]}]
    整合每个词语的句法依存关系---->format_parse_list =  [['SBV', '我', 0, 'r', '购买', 0, 'r'], ['HED', '购买', 1, 'v', 'Root', -2, 'd'], ['RAD', '了', 2, 'u', '购买', 0, 'r'], ['ATT', '一', 3, 'm', '件', 3, 'm'], ['ATT', '件', 4, 'q', '玩具', 4, 'q'], ['VOB', '玩具', 5, 'n', '购买', 0, 'r'], ['WP', '，', 6, 'wp', '购买', 0, 'r'], ['SBV', '孩子', 7, 'n', '喜欢', 8, 'd'], ['ADV', '非常', 8, 'd', '喜欢', 8, 'd'], ['COO', '喜欢', 9, 'v', '购买', 0, 'r'], ['ATT', '这个', 10, 'r', '玩具', 10, 'r'], ['VOB', '玩具', 11, 'n', '喜欢', 8, 'd'], ['WP', '，', 12, 'wp', '喜欢', 8, 'd'], ['ADV', '但是', 13, 'c', '好', 16, 'd'], ['SBV', '质量', 14, 'n', '好', 16, 'd'], ['ADV', '不', 15, 'd', '好', 16, 'd'], ['ADV', '太', 16, 'd', '好', 16, 'd'], ['COO', '好', 17, 'a', '喜欢', 8, 'd']]
    -------------------------------------------------- 依存句法分析：结束 --------------------------------------------------
    -------------------------------------------------- 语义角色标注：开始 --------------------------------------------------
    分词----> words= ['我', '购买', '了', '一', '件', '玩具', '，', '孩子', '非常', '喜欢', '这个', '玩具', '，', '但是', '质量', '不', '太', '好']----len(words) = 18
    词性标注----> postags= ['r', 'v', 'u', 'm', 'q', 'n', 'wp', 'n', 'd', 'v', 'r', 'n', 'wp', 'c', 'n', 'd', 'd', 'a']----len(postags) = 18
    len(roles) = 2----roles = <pyltp.SementicRoles object at 0x0000026FF8D2E870>
    谓语所在索引：role.index = 1
    谓语所在索引：role.index = 17
    语义角色标注---->roles_dict = {
        1: {
        'A0': ['A0', 0, 0], 'A1': ['A1', 3, 5]}, 17: {
        'DIS': ['DIS', 13, 13], 'A0': ['A0', 14, 14], 'ADV': ['ADV', 16, 16]}}
    -------------------------------------------------- 语义角色标注：结束 --------------------------------------------------
    svo = [['我', '购买', '一件玩具'], ['孩子', '喜欢', '这个玩具']]
    ================================================== 第1句：结束 ==================================================
    ================================================== 第2句：开始 ==================================================
    -------------------------------------------------- 依存句法分析：开始 --------------------------------------------------
    分词列表：words = ['希望', '商家', '能够', '保障', '商品', '质量', '，', '不要', '再', '出现', '类似', '问题']
    词性分析：postags = ['v', 'n', 'v', 'v', 'n', 'n', 'wp', 'd', 'd', 'v', 'v', 'n']
    各个词语所依赖的父节点：rely_ids = [-1, 3, 3, 0, 5, 3, 3, 9, 9, 3, 11, 9]
    各个词语所依赖的父节点词语 = ['Root', '保障', '保障', '希望', '质量', '保障', '保障', '出现', '出现', '保障', '问题', '出现']
    各个词语与所依赖的父节点的依赖关系 = ['HED', 'SBV', 'ADV', 'VOB', 'ATT', 'VOB', 'WP', 'ADV', 'ADV', 'COO', 'ATT', 'VOB']
    
    word_index = 0----word = 希望
    word_index = 0----arc_index = 3----rely_ids[arc_index] = 0----relations[arc_index] = VOB
    child_dict = {
        'VOB': [3]}
    child_dict_list = [{
        'VOB': [3]}]
    
    word_index = 1----word = 商家
    child_dict_list = [{
        'VOB': [3]}, {
        }]
    
    word_index = 2----word = 能够
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }]
    
    word_index = 3----word = 保障
    word_index = 3----arc_index = 1----rely_ids[arc_index] = 3----relations[arc_index] = SBV
    child_dict = {
        'SBV': [1]}
    word_index = 3----arc_index = 2----rely_ids[arc_index] = 3----relations[arc_index] = ADV
    child_dict = {
        'SBV': [1], 'ADV': [2]}
    word_index = 3----arc_index = 5----rely_ids[arc_index] = 3----relations[arc_index] = VOB
    child_dict = {
        'SBV': [1], 'ADV': [2], 'VOB': [5]}
    word_index = 3----arc_index = 6----rely_ids[arc_index] = 3----relations[arc_index] = WP
    child_dict = {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6]}
    word_index = 3----arc_index = 9----rely_ids[arc_index] = 3----relations[arc_index] = COO
    child_dict = {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}]
    
    word_index = 4----word = 商品
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }]
    
    word_index = 5----word = 质量
    word_index = 5----arc_index = 4----rely_ids[arc_index] = 5----relations[arc_index] = ATT
    child_dict = {
        'ATT': [4]}
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        'ATT': [4]}]
    
    word_index = 6----word = ，
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        'ATT': [4]}, {
        }]
    
    word_index = 7----word = 不要
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        'ATT': [4]}, {
        }, {
        }]
    
    word_index = 8----word = 再
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        'ATT': [4]}, {
        }, {
        }, {
        }]
    
    word_index = 9----word = 出现
    word_index = 9----arc_index = 7----rely_ids[arc_index] = 9----relations[arc_index] = ADV
    child_dict = {
        'ADV': [7]}
    word_index = 9----arc_index = 8----rely_ids[arc_index] = 9----relations[arc_index] = ADV
    child_dict = {
        'ADV': [7, 8]}
    word_index = 9----arc_index = 11----rely_ids[arc_index] = 9----relations[arc_index] = VOB
    child_dict = {
        'ADV': [7, 8], 'VOB': [11]}
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'ADV': [7, 8], 'VOB': [11]}]
    
    word_index = 10----word = 类似
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'ADV': [7, 8], 'VOB': [11]}, {
        }]
    
    word_index = 11----word = 问题
    word_index = 11----arc_index = 10----rely_ids[arc_index] = 11----relations[arc_index] = ATT
    child_dict = {
        'ATT': [10]}
    child_dict_list = [{
        'VOB': [3]}, {
        }, {
        }, {
        'SBV': [1], 'ADV': [2], 'VOB': [5], 'WP': [6], 'COO': [9]}, {
        }, {
        'ATT': [4]}, {
        }, {
        }, {
        }, {
        'ADV': [7, 8], 'VOB': [11]}, {
        }, {
        'ATT': [10]}]
    整合每个词语的句法依存关系---->format_parse_list =  [['HED', '希望', 0, 'v', 'Root', -2, 'v'], ['SBV', '商家', 1, 'n', '保障', 2, 'v'], ['ADV', '能够', 2, 'v', '保障', 2, 'v'], ['VOB', '保障', 3, 'v', '希望', -1, 'n'], ['ATT', '商品', 4, 'n', '质量', 4, 'n'], ['VOB', '质量', 5, 'n', '保障', 2, 'v'], ['WP', '，', 6, 'wp', '保障', 2, 'v'], ['ADV', '不要', 7, 'd', '出现', 8, 'd'], ['ADV', '再', 8, 'd', '出现', 8, 'd'], ['COO', '出现', 9, 'v', '保障', 2, 'v'], ['ATT', '类似', 10, 'v', '问题', 10, 'v'], ['VOB', '问题', 11, 'n', '出现', 8, 'd']]
    -------------------------------------------------- 依存句法分析：结束 --------------------------------------------------
    -------------------------------------------------- 语义角色标注：开始 --------------------------------------------------
    分词----> words= ['希望', '商家', '能够', '保障', '商品', '质量', '，', '不要', '再', '出现', '类似', '问题']----len(words) = 12
    词性标注----> postags= ['v', 'n', 'v', 'v', 'n', 'n', 'wp', 'd', 'd', 'v', 'v', 'n']----len(postags) = 12
    len(roles) = 4----roles = <pyltp.SementicRoles object at 0x0000026FF8D2E870>
    谓语所在索引：role.index = 0
    谓语所在索引：role.index = 3
    谓语所在索引：role.index = 7
    谓语所在索引：role.index = 9
    语义角色标注---->roles_dict = {
        0: {
        'A1': ['A1', 1, 11]}, 3: {
        'A0': ['A0', 1, 1], 'A1': ['A1', 4, 5]}, 7: {
        'A0': ['A0', 1, 1]}, 9: {
        'ADV': ['ADV', 8, 8], 'A1': ['A1', 10, 11]}}
    -------------------------------------------------- 语义角色标注：结束 --------------------------------------------------
    svo = [['商家', '保障', '商品质量']]
    ================================================== 第2句：结束 ==================================================
    关系抽取结果：svos = [['我', '购买', '一件玩具'], ['孩子', '喜欢', '这个玩具'], ['商家', '保障', '商品质量']]
    
    Process finished with exit code 0

## 四、构建知识图谱 ##

通过上一步骤的关系抽取的三元组结果【`A0（触发者）`、`谓语词`、`A1（受事者）`】就可以创建知识图谱的实体与关系。