《知识图谱从入门到实践》06 基于Jieba词性标注获取文本三元组关系
书山有路勤为径，学海无涯苦作舟
1.基于Jieba实现文本的三元组抽取
定义函数
# coding=utf-8
import re, os
import jieba.posseg as pseg
class ExtractEvent:
def __init__(self):
self.map_dict = self.load_mapdict()
self.minlen = 2
self.maxlen = 30
self.keywords_num = 20
self.limit_score = 10
self.IP = "(([NERMQ]*P*[ABDP]*)*([ABDV]{1,})*([NERMQ]*)*([VDAB]$)?([NERMQ]*)*([VDAB]$)?)*"
self.IP = "([NER]*([PMBQADP]*[NER]*)*([VPDA]{1,}[NEBRVMQDA]*)*)"
self.MQ = '[DP]*M{1,}[Q]*([VN]$)?'
self.VNP = 'V*N{1,}'
self.NP = '[NER]{1,}'
self.REN = 'R{2,}'
self.VP = 'P?(V|A$|D$){1,}'
self.PP = 'P?[NERMQ]{1,}'
self.SPO_n = "n{1,}"
self.SPO_v = "v{1,}"
self.stop_tags = {'u', 'wp', 'o', 'y', 'w', 'f', 'u', 'c', 'uj', 'nd', 't', 'x'}
self.combine_words = {"首先", "然后", "之前", "之后", "其次", "接着"}
"""构建映射字典"""
def load_mapdict(self):
tag_dict = {
'B': 'b'.split(), # 时间词
'A': 'a d'.split(), # 时间词
'D': "d".split(), # 限定词
'N': "n j s zg en l r".split(), #名词
"E": "nt nz ns an ng".split(), #实体词
"R": "nr".split(), #人物
'G': "g".split(), #语素
'V': "vd v va i vg vn g".split(), #动词
'P': "p f".split(), #介词
"M": "m t".split(), #数词
"Q": "q".split(), #量词
"v": "V".split(), #动词短语
"n": "N".split(), #名词介宾短语
}
map_dict = {}
for flag, tags in tag_dict.items():
for tag in tags:
map_dict[tag] = flag
return map_dict
"""根据定义的标签,对词性进行标签化"""
def transfer_tags(self, postags):
tags = [self.map_dict.get(tag[:2], 'W') for tag in postags]
return ''.join(tags)
"""抽取出指定长度的ngram"""
def extract_ngram(self, pos_seq, regex):
ss = self.transfer_tags(pos_seq)
def gen():
for s in range(len(ss)):
for n in range(self.minlen, 1 + min(self.maxlen, len(ss) - s)):
e = s + n
substr = ss[s:e]
if re.match(regex + "$", substr):
yield (s, e)
return list(gen())
'''抽取ngram'''
def extract_sentgram(self, pos_seq, regex):
ss = self.transfer_tags(pos_seq)
def gen():
for m in re.finditer(regex, ss):
yield (m.start(), m.end())
return list(gen())
"""指示代词替换，消解处理"""
def cite_resolution(self, words, postags, persons):
if not persons and 'r' not in set(postags):
return words, postags
elif persons and 'r' in set(postags):
cite_index = postags.index('r')
if words[cite_index] in {"其", "他", "她", "我"}:
words[cite_index] = persons[-1]
postags[cite_index] = 'nr'
elif 'r' in set(postags):
cite_index = postags.index('r')
if words[cite_index] in {"为何", "何", "如何"}:
postags[cite_index] = 'w'
return words, postags
"""抽取量词性短语"""
def extract_mqs(self, wds, postags):
phrase_tokspans = self.extract_sentgram(postags, self.MQ)
if not phrase_tokspans:
return []
phrases = [''.join(wds[i[0]:i[1]])for i in phrase_tokspans]
return phrases
'''抽取动词性短语'''
def get_ips(self, wds, postags):
ips = []
phrase_tokspans = self.extract_sentgram(postags, self.IP)
if not phrase_tokspans:
return []
phrases = [''.join(wds[i[0]:i[1]])for i in phrase_tokspans]
phrase_postags = [''.join(postags[i[0]:i[1]]) for i in phrase_tokspans]
for phrase, phrase_postag_ in zip(phrases, phrase_postags):
if not phrase:
continue
phrase_postags = ''.join(phrase_postag_).replace('m', '').replace('q','').replace('a', '').replace('t', '')
if phrase_postags.startswith('n') or phrase_postags.startswith('j'):
has_subj = 1
else:
has_subj = 0
ips.append((has_subj, phrase))
return ips
"""分短句处理"""
def split_short_sents(self, text):
return [i for i in re.split(r'[,，]', text) if len(i)>2]
"""分段落"""
def split_paras(self, text):
return [i for i in re.split(r'[\n\r]', text) if len(i) > 4]
"""分长句处理"""
def split_long_sents(self, text):
return [i for i in re.split(r'[;。:； ：？?!！【】▲丨|]', text) if len(i) > 4]
"""移出噪声数据"""
def remove_punc(self, text):
text = text.replace('\u3000', '').replace("'", '').replace('“', '').replace('”', '').replace('▲','').replace('” ', "”")
tmps = re.findall('[\(|（][^\(（\)）]*[\)|）]', text)
for tmp in tmps:
text = text.replace(tmp, '')
return text
"""保持专有名词"""
def zhuanming(self, text):
books = re.findall('[<《][^《》]*[》>]', text)
return books
"""对人物类词语进行修正"""
def modify_nr(self, wds, postags):
phrase_tokspans = self.extract_sentgram(postags, self.REN)
wds_seq = ' '.join(wds)
pos_seq = ' '.join(postags)
if not phrase_tokspans:
return wds, postags
else:
wd_phrases = [' '.join(wds[i[0]:i[1]]) for i in phrase_tokspans]
postag_phrases = [' '.join(postags[i[0]:i[1]]) for i in phrase_tokspans]
for wd_phrase in wd_phrases:
tmp = wd_phrase.replace(' ', '')
wds_seq = wds_seq.replace(wd_phrase, tmp)
for postag_phrase in postag_phrases:
pos_seq = pos_seq.replace(postag_phrase, 'nr')
words = [i for i in wds_seq.split(' ') if i]
postags = [i for i in pos_seq.split(' ') if i]
return words, postags
"""对人物类词语进行修正"""
def modify_duplicate(self, wds, postags, regex, tag):
phrase_tokspans = self.extract_sentgram(postags, regex)
wds_seq = ' '.join(wds)
pos_seq = ' '.join(postags)
if not phrase_tokspans:
return wds, postags
else:
wd_phrases = [' '.join(wds[i[0]:i[1]]) for i in phrase_tokspans]
postag_phrases = [' '.join(postags[i[0]:i[1]]) for i in phrase_tokspans]
for wd_phrase in wd_phrases:
tmp = wd_phrase.replace(' ', '')
wds_seq = wds_seq.replace(wd_phrase, tmp)
for postag_phrase in postag_phrases:
pos_seq = pos_seq.replace(postag_phrase, tag)
words = [i for i in wds_seq.split(' ') if i]
postags = [i for i in pos_seq.split(' ') if i]
return words, postags
'''对句子进行分词处理'''
def cut_wds(self, sent):
wds = list(pseg.cut(sent))
postags = [w.flag for w in wds]
words = [w.word for w in wds]
return self.modify_nr(words, postags)
"""移除噪声词语"""
def clean_wds(self, words, postags):
wds = []
poss =[]
for wd, postag in zip(words, postags):
if postag[0].lower() in self.stop_tags:
continue
wds.append(wd)
poss.append(postag[:2])
return wds, poss
"""检测是否成立, 肯定需要包括名词"""
def check_flag(self, postags):
if not {"v", 'a', 'i'}.intersection(postags):
return 0
return 1
"""识别出人名实体"""
def detect_person(self, words, postags):
persons = []
for wd, postag in zip(words, postags):
if postag == 'nr':
persons.append(wd)
return persons
"""识别出名词性短语"""
def get_nps(self, wds, postags):
phrase_tokspans = self.extract_sentgram(postags, self.NP)
if not phrase_tokspans:
return [],[]
phrases_np = [''.join(wds[i[0]:i[1]]) for i in phrase_tokspans]
return phrase_tokspans, phrases_np
"""识别出介宾短语"""
def get_pps(self, wds, postags):
phrase_tokspans = self.extract_sentgram(postags, self.PP)
if not phrase_tokspans:
return [],[]
phrases_pp = [''.join(wds[i[0]:i[1]]) for i in phrase_tokspans]
return phrase_tokspans, phrases_pp
"""识别出动词短语"""
def get_vps(self, wds, postags):
phrase_tokspans = self.extract_sentgram(postags, self.VP)
if not phrase_tokspans:
return [],[]
phrases_vp = [''.join(wds[i[0]:i[1]]) for i in phrase_tokspans]
return phrase_tokspans, phrases_vp
"""抽取名动词性短语"""
def get_vnps(self, s):
wds, postags = self.cut_wds(s)
if not postags:
return [], []
if not (postags[-1].endswith("n") or postags[-1].endswith("l") or postags[-1].endswith("i")):
return [], []
phrase_tokspans = self.extract_sentgram(postags, self.VNP)
if not phrase_tokspans:
return [], []
phrases_vnp = [''.join(wds[i[0]:i[1]]) for i in phrase_tokspans]
phrase_tokspans2 = self.extract_sentgram(postags, self.NP)
if not phrase_tokspans2:
return [], []
phrases_np = [''.join(wds[i[0]:i[1]]) for i in phrase_tokspans2]
return phrases_vnp, phrases_np
"""提取短语"""
def phrase_ip(self, content):
try:
spos = []
events = []
content = self.remove_punc(content)
paras = self.split_paras(content)
for para in paras:
long_sents = self.split_long_sents(para)
for long_sent in long_sents:
persons = []
short_sents = self.split_short_sents(long_sent)
for sent in short_sents:
words, postags = self.cut_wds(sent)
person = self.detect_person(words, postags)
words, postags = self.cite_resolution(words, postags, persons)
words, postags = self.clean_wds(words, postags)
#print(words,postags)
ips = self.get_ips(words, postags)
persons += person
for ip in ips:
events.append(ip[1])
wds_tmp = []
postags_tmp = []
words, postags = self.cut_wds(ip[1])
verb_tokspans, verbs = self.get_vps(words, postags)
pp_tokspans, pps = self.get_pps(words, postags)
tmp_dict = {str(verb[0]) + str(verb[1]): ['V', verbs[idx]] for idx, verb in enumerate(verb_tokspans)}
pp_dict = {str(pp[0]) + str(pp[1]): ['N', pps[idx]] for idx, pp in enumerate(pp_tokspans)}
tmp_dict.update(pp_dict)
sort_keys = sorted([int(i) for i in tmp_dict.keys()])
for i in sort_keys:
# print(i)
if i < 10:
i = '0' + str(i)
wds_tmp.append(tmp_dict[str(i)][-1])
postags_tmp.append(tmp_dict[str(i)][0])
wds_tmp, postags_tmp = self.modify_duplicate(wds_tmp, postags_tmp, self.SPO_v, 'V')
wds_tmp, postags_tmp = self.modify_duplicate(wds_tmp, postags_tmp, self.SPO_n, 'N')
if len(postags_tmp) < 2:
continue
seg_index = []
i = 0
for wd, postag in zip(wds_tmp, postags_tmp):
if postag == 'V':
seg_index.append(i)
i += 1
spo = []
for indx, seg_indx in enumerate(seg_index):
if indx == 0:
pre_indx = 0
else:
pre_indx = seg_index[indx-1]
if pre_indx < 0:
pre_indx = 0
if seg_indx == 0:
spo.append(('', wds_tmp[seg_indx], ''.join(wds_tmp[seg_indx+1:])))
elif seg_indx > 0 and indx < 1:
spo.append((''.join(wds_tmp[:seg_indx]), wds_tmp[seg_indx], ''.join(wds_tmp[seg_indx + 1:])))
else:
spo.append((''.join(wds_tmp[pre_indx+1:seg_indx]), wds_tmp[seg_indx], ''.join(wds_tmp[seg_indx + 1:])))
spos += spo
except:
print('报错')
return events, spos
主函数路口
import time
handler = ExtractEvent()
start = time.time()
content='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。'
events, spos = handler.phrase_ip(content)
spos = [i for i in spos if i[0] and i[2]]
for spo in spos:
print(spo)
输出
