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QA_HanLP.py
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QA_HanLP.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'lbbxsxlz'
import threading
import time
#加载HanLP
from jpype import *
'''
CONLL标注格式包含10列，分别为：
———————————————————————————
ID FORM LEMMA CPOSTAG POSTAG FEATS HEAD DEPREL PHEAD PDEPREL
———————————————————————————
本次评测只用到前８列，其含义分别为：
1 ID 当前词在句子中的序号，１开始.
2 FORM 当前词语或标点
3 LEMMA 当前词语（或标点）的原型或词干，在中文中，此列与FORM相同
4 CPOSTAG 当前词语的词性（粗粒度）
5 POSTAG 当前词语的词性（细粒度）
6 FEATS 句法特征，在本次评测中，此列未被使用，全部以下划线代替。
7 HEAD 当前词语的中心词
8 DEPREL 当前词语与中心词的依存关系
在CONLL格式中，每个词语占一行，无值列用下划线'_'代替，列的分隔符为制表符'\t'，行的分隔符为换行符'\n'；句子与句子之间用空行分隔。
'''
'''
#依存标签
HED = "核心关系"
SBV = "主谓关系"
VOB = "动宾关系"
IOB = "间宾关系"
FOB = "前置宾语"
DBL = "兼语"
ATT = "定中关系"
ADV = "状中结构"
CMP = "动补结构"
COO = "并列关系"
POB = "介宾关系"
LAD = "左附加关系"
RAD = "右附加关系"
IS = "独立结构"
WP = "标点符号"
'''
startJVM(getDefaultJVMPath(), "-Djava.class.path=D:\workspace\HanLP\hanlp-1.2.8-release\hanlp-1.2.8.jar;D:\workspace\HanLP\hanlp-1.2.8-release")
HanLP = JClass('com.hankcs.hanlp.HanLP')
#语法依存分析
DependencyParser = JClass('com.hankcs.hanlp.dependency.nnparser.NeuralNetworkDependencyParser')
#CoNLLWord = JClass('com.hankcs.hanlp.corpus.dependency.CoNll.CoNLLWord')
#CoNLLSentence = JClass('com.hankcs.hanlp.corpus.dependency.CoNll.CoNLLSentence')
#分词，包含在HanLP.segment中
#NLPTokenizer = JClass('com.hankcs.hanlp.tokenizer.NLPTokenizer')
Suggester = JClass('com.hankcs.hanlp.suggest.Suggester')
# 依存句法分析
def parser_deal(string):
print("--------依存句法分析-------- ")
print("HanLP依存句法分析 ")
#依存标签中文标识
#csentence = HanLP.parseDependency(str)
#t0 = time.clock()
#依存标签英文标识
Parser = DependencyParser().enableDeprelTranslator(False)
#print("init take time %s" %(time.clock() - t0))
csentence = Parser.parse(string)
print(csentence)
global wordArray
wordArray = csentence.getWordArray()
for cword in wordArray:
print('%s -- %s -- %s' %(cword.LEMMA, cword.DEPREL, cword.HEAD.LEMMA))
# 创建文本推荐对象
def sentence_suggester():
global suggester
suggester = Suggester()
file = open("qa.txt", "rb")
print("==========文本内容========")
for line in file:
#print(line)
sentence = line.decode('utf-8')
print(sentence,end = "")
suggester.addSentence(sentence)
file.close()
print("\n")
# 依存句法分析，首次调用耗时较久，
# 之后调用就不再耗时，目前原因未知
# 估计与Java类型转换到python类型有关系
parser_deal("依存句法分析")
#sentence_suggester()
n = 1
while(n > 0):
str = input("please input a sentence or input quit to exit \n")
#str = "世界上最高的山峰是哪座山？"
if str == "quit":
break
#elif str == "":
elif len(str) < 1:
print(len(str))
continue
print(40*"=")
print("HanLP分词与词性标注 ")
print(HanLP.segment(str))
#str_list = HanLP.segment(str)
#for a in str_list:
# print(a)
#print(NLPTokenizer.segment(str))
#创建线程
#td = threading.Thread(target=parser_deal(str))
#td.start()
#td.join()
t1 = time.clock()
parser_deal(str)
print("parser_deal take time %s" %(time.clock() - t1))
print("--------关键词抽取--------")
print("HanLP关键词抽取 ")
keywords = HanLP.extractKeyword(str, 4)
print(keywords)
if len(keywords) < 1:
print("无法提取到关键字")
continue
print("HanLP短语抽取 ")
phrase = HanLP.extractPhrase(str, 2)
print(phrase)
if len(phrase) >= 1:
key = phrase[0]
elif len(phrase) < 1 and len(keywords) >= 1:
key = keywords[0]
print(key)
for word in wordArray:
if(word.DEPREL == "SBV"):
key_word = word.LEMMA
print(key_word)
for s_word in wordArray:
if (s_word.DEPREL == "ATT" and s_word.HEAD.LEMMA == key_word):
key_word_2 = s_word.LEMMA
print(key_word_2)
KEY = key_word_2 + key_word
print(KEY)
break
break
#print("========根据关键字找类似的句子========")
#print(suggester.suggest(key,1))
shutdownJVM()