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sentence_parser.py
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sentence_parser.py
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#!/usr/bin/env python3
# coding: utf-8
# File: sentence_parser.py
# Author: lhy<[email protected],https://huangyong.github.io>
# Date: 18-3-10
import os
from pyhanlp import *
class LtpParser():
'''基本操作'''
'''postags, netags = self.basic_parser(words)'''
def basic_parser(self, words):
postags = list(self.postagger.postag(words))
#postage= [str(term.nature) for term in self.HanLP.segment((words))]
netags = self.recognizer.recognize(words, postags)
return postags, netags
'''ltp获取词性'''
def get_postag(self, words):
return list(self.postagger.postag(words))
'''基于实体识别结果,整理输出实体列表'''
'''name_entity_dist = self.format_entity(words, netags, postags)'''
def format_entity(self, words, postags):
name_entity_dist = {}
name_entity_list = []
place_entity_list = []
organization_entity_list = []
ntag_E_Nh = ""
ntag_E_Ni = ""
ntag_E_Ns = ""
index = 0
for item in zip(words, postags):
word = item[0]
ptag = item[1]
if ptag == 'nr':
name_entity_list.append(word+'_%s ' % index)
elif ptag == 'nt':
organization_entity_list.append(word+'_%s ' % index)
elif ptag == 'ns':
place_entity_list.append(word + '_%s ' % index)
index += 1
name_entity_dist['nhs'] = name_entity_list
name_entity_dist['nis'] = organization_entity_list
name_entity_dist['nss'] = place_entity_list
return name_entity_dist
'''entity修正,为rebuild_wordspostags做准备'''
def modify_entity(self, entity_list, words, postags, tag):
entity_modify = []
if entity_list:
for entity in entity_list:
entity_dict = {}
subs = entity.split(' ')[:-1]
start_index = subs[0].split('_')[1]
end_index = subs[-1].split('_')[1]
entity_dict['stat_index'] = start_index
entity_dict['end_index'] = end_index
if start_index == entity_dict['end_index']:
consist = [words[int(start_index)] + '/' + postags[int(start_index)]]
else:
consist = [words[index] + '/' + postags[index] for index in range(int(start_index), int(end_index)+1)]
entity_dict['consist'] = consist
entity_dict['name'] = ''.join(tmp.split('_')[0] for tmp in subs) + '/' + tag
entity_modify.append(entity_dict)
return entity_modify
'''基于命名实体识别,修正words,postags'''
'''words, postags = self.rebuild_wordspostags(name_entity_dist, words, postags)'''
def rebuild_wordspostags(self, name_entity_dist, words, postags):
pre = ' '.join([item[0] + '/' + item[1] for item in zip(words, postags)])
post = pre
for et, infos in name_entity_dist.items():
if infos:
for info in infos:
post = post.replace(' '.join(info['consist']), info['name'])
post = [word for word in post.split(' ') if len(word.split('/')) == 2 and word.split('/')[0]]
words = [tmp.split('/')[0] for tmp in post]
postags = [tmp.split('/')[1] for tmp in post]
return words, postags
'''依存关系格式化'''
'''tuples = self.syntax_parser(words, postags)'''
def syntax_parser(self, words, postags,sentence):
sentence_parse = HanLP.parseDependency(sentence)
word_array = sentence_parse.getWordArray()
arcs = [str(word.HEAD).split('\t')[0] for word in word_array]
relation = [str(word.DEPREL) for word in word_array]
tuples = list()
for index in range(len(words)-1):
arc_index = arcs[index]
tuples.append([index+1, words[index], postags[index], words[arc_index], postags[arc_index], arc_index, arc_relation[index]])
return tuples
'''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'''
'''child_dict_list = self.build_parse_child_dict(words, postags, tuples)'''
def build_parse_child_dict(self, words, postags, tuples):
child_dict_list = list()
for index, word in enumerate(words):
child_dict = dict()
for arc in tuples:
if arc[3] == word:
if arc[-1] in child_dict:
child_dict[arc[-1]].append(arc)
else:
child_dict[arc[-1]] = []
child_dict[arc[-1]].append(arc)
child_dict_list.append([word, postags[index], index, child_dict])
return child_dict_list
'''parser主函数'''
'''抽取出事件三元组'''
'''tuples, child_dict_list = self.parser.parser_main(words, postags)'''
def parser_main(self, words, postags,sentence):
tuples = self.syntax_parser(words, postags,sentence)
child_dict_list = self.build_parse_child_dict(words, postags, tuples)
return tuples, child_dict_list
'''基础语言分析'''
'''对句子进行分词，词性标注处理'''
'''words, postags = self.parser.basic_process(sent)'''
def basic_process(self, sentence):
words=[str(term.word) for term in self.HanLP.segment((sentence))]
postage= [str(term.nature) for term in self.HanLP.segment((sentence))]
name_entity_dist = self.format_entity(words, postags)
#words, postags = self.rebuild_wordspostags(name_entity_dist, words, postags)
return words, postags