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fact_triple_extraction.py
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fact_triple_extraction.py
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#!/usr/bin/env python
# coding=utf-8
"""
文本中事实三元组抽取
python *.py input.txt output.txt begin_line end_line
"""
__author__ = "tianwen jiang"
# Set your own model path
MODELDIR="/data/ltp/ltp-models/3.3.0/ltp_data"
import sys
import os
from pyltp import Segmentor, Postagger, Parser, NamedEntityRecognizer
print "正在加载LTP模型... ..."
segmentor = Segmentor()
segmentor.load(os.path.join(MODELDIR, "cws.model"))
postagger = Postagger()
postagger.load(os.path.join(MODELDIR, "pos.model"))
parser = Parser()
parser.load(os.path.join(MODELDIR, "parser.model"))
recognizer = NamedEntityRecognizer()
recognizer.load(os.path.join(MODELDIR, "ner.model"))
#labeller = SementicRoleLabeller()
#labeller.load(os.path.join(MODELDIR, "srl/"))
print "加载模型完毕。"
in_file_name = "input.txt"
out_file_name = "output.txt"
begin_line = 1
end_line = 0
if len(sys.argv) > 1:
in_file_name = sys.argv[1]
if len(sys.argv) > 2:
out_file_name = sys.argv[2]
if len(sys.argv) > 3:
begin_line = int(sys.argv[3])
if len(sys.argv) > 4:
end_line = int(sys.argv[4])
def extraction_start(in_file_name, out_file_name, begin_line, end_line):
"""
事实三元组抽取的总控程序
Args:
in_file_name: 输入文件的名称
#out_file_name: 输出文件的名称
begin_line: 读文件的起始行
end_line: 读文件的结束行
"""
in_file = open(in_file_name, 'r')
out_file = open(out_file_name, 'a')
line_index = 1
sentence_number = 0
text_line = in_file.readline()
while text_line:
if line_index < begin_line:
text_line = in_file.readline()
line_index += 1
continue
if end_line != 0 and line_index > end_line:
break
sentence = text_line.strip()
if sentence == "" or len(sentence) > 1000:
text_line = in_file.readline()
line_index += 1
continue
try:
fact_triple_extract(sentence, out_file)
out_file.flush()
except:
pass
sentence_number += 1
if sentence_number % 50 == 0:
print "%d done" % (sentence_number)
text_line = in_file.readline()
line_index += 1
in_file.close()
out_file.close()
def fact_triple_extract(sentence, out_file):
"""
对于给定的句子进行事实三元组抽取
Args:
sentence: 要处理的语句
"""
#print sentence
words = segmentor.segment(sentence)
#print "\t".join(words)
postags = postagger.postag(words)
netags = recognizer.recognize(words, postags)
arcs = parser.parse(words, postags)
#print "\t".join("%d:%s" % (arc.head, arc.relation) for arc in arcs)
child_dict_list = build_parse_child_dict(words, postags, arcs)
for index in range(len(postags)):
# 抽取以谓词为中心的事实三元组
if postags[index] == 'v':
child_dict = child_dict_list[index]
# 主谓宾
if child_dict.has_key('SBV') and child_dict.has_key('VOB'):
e1 = complete_e(words, postags, child_dict_list, child_dict['SBV'][0])
r = words[index]
e2 = complete_e(words, postags, child_dict_list, child_dict['VOB'][0])
out_file.write("主语谓语宾语关系\t(%s, %s, %s)\n" % (e1, r, e2))
out_file.flush()
# 定语后置，动宾关系
if arcs[index].relation == 'ATT':
if child_dict.has_key('VOB'):
e1 = complete_e(words, postags, child_dict_list, arcs[index].head - 1)
r = words[index]
e2 = 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:
out_file.write("定语后置动宾关系\t(%s, %s, %s)\n" % (e1, r, e2))
out_file.flush()
# 含有介宾关系的主谓动补关系
if child_dict.has_key('SBV') and child_dict.has_key('CMP'):
#e1 = words[child_dict['SBV'][0]]
e1 = complete_e(words, postags, child_dict_list, child_dict['SBV'][0])
cmp_index = child_dict['CMP'][0]
r = words[index] + words[cmp_index]
if child_dict_list[cmp_index].has_key('POB'):
e2 = complete_e(words, postags, child_dict_list, child_dict_list[cmp_index]['POB'][0])
out_file.write("介宾关系主谓动补\t(%s, %s, %s)\n" % (e1, r, e2))
out_file.flush()
# 尝试抽取命名实体有关的三元组
if netags[index][0] == 'S' or netags[index][0] == 'B':
ni = index
if netags[ni][0] == 'B':
while netags[ni][0] != 'E':
ni += 1
e1 = ''.join(words[index:ni+1])
else:
e1 = words[ni]
if arcs[ni].relation == 'ATT' and postags[arcs[ni].head-1] == 'n' and netags[arcs[ni].head-1] == 'O':
r = complete_e(words, postags, child_dict_list, arcs[ni].head-1)
if e1 in r:
r = r[(r.index(e1)+len(e1)):]
if arcs[arcs[ni].head-1].relation == 'ATT' and netags[arcs[arcs[ni].head-1].head-1] != 'O':
e2 = complete_e(words, postags, child_dict_list, arcs[arcs[ni].head-1].head-1)
mi = arcs[arcs[ni].head-1].head-1
li = mi
if netags[mi][0] == 'B':
while netags[mi][0] != 'E':
mi += 1
e = ''.join(words[li+1:mi+1])
e2 += e
if r in e2:
e2 = e2[(e2.index(r)+len(r)):]
if r+e2 in sentence:
out_file.write("人名//地名//机构\t(%s, %s, %s)\n" % (e1, r, e2))
out_file.flush()
def build_parse_child_dict(words, postags, arcs):
"""
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
Args:
words: 分词列表
postags: 词性列表
arcs: 句法依存列表
"""
child_dict_list = []
for index in range(len(words)):
child_dict = dict()
for arc_index in range(len(arcs)):
if arcs[arc_index].head == index + 1:
if child_dict.has_key(arcs[arc_index].relation):
child_dict[arcs[arc_index].relation].append(arc_index)
else:
child_dict[arcs[arc_index].relation] = []
child_dict[arcs[arc_index].relation].append(arc_index)
#if child_dict.has_key('SBV'):
# print words[index],child_dict['SBV']
child_dict_list.append(child_dict)
return child_dict_list
def complete_e(words, postags, child_dict_list, word_index):
"""
完善识别的部分实体
"""
child_dict = child_dict_list[word_index]
prefix = ''
if child_dict.has_key('ATT'):
for i in range(len(child_dict['ATT'])):
prefix += complete_e(words, postags, child_dict_list, child_dict['ATT'][i])
postfix = ''
if postags[word_index] == 'v':
if child_dict.has_key('VOB'):
postfix += complete_e(words, postags, child_dict_list, child_dict['VOB'][0])
if child_dict.has_key('SBV'):
prefix = complete_e(words, postags, child_dict_list, child_dict['SBV'][0]) + prefix
return prefix + words[word_index] + postfix
if __name__ == "__main__":
#extraction_start(in_file_name, out_file_name, begin_line, end_line)
extraction_start(in_file_name, out_file_name, begin_line, end_line)