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predict.py
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import re
import jieba
import jieba.posseg as pseg
jieba.load_userdict('./source/entity.txt')
# pku的模型略好
# jiagu.load_model('./model/pku.model') # 使用国家语委分词标准
class Toc:
index = 0
# 获取停用词的路径
stop = r'./source/stop_word.txt'
# 停用词表
stop_list = []
def get_stop_word(self):
with open(self.stop, 'r', encoding='utf-8') as f:
self.stop_list = [line.strip('\n') for line in f.readlines()]
@staticmethod
def part_re():
"""
添加分层次结构的正则表达
:return:
"""
part_re_d = re.compile(r"\d\.\d", re.S)
return part_re_d
def get_struction(self):
"""
对文本信息进行一个归类的处理 分出层次结构
:param:
:return:
"""
path = r'./data_file/part_' + str(self.index) + '.txt'
with open(path, 'r', encoding='utf-8') as f:
all_data = [line.strip('\n').replace(" ", '').replace(' ', '').replace("·", "_").replace('-', '').replace('*', '').replace('与', '-').replace('、', '-').replace('和', '-').replace('从', '&').replace('看', '&') for line in f.readlines()]
parts = self.part_re() # 获取正则匹配的表达式
flag = re.findall(parts, all_data[1]) # 设定初始游标判断是否进行下一个的输入
result = [] # 存贮总的信息
f = [] # 存储当前单元的信息 如 ['1.1信息', '1.1.1信息的定义', '1.1.2信息的种类', '1.1.3信息的度量']
for data in all_data:
part_result = re.findall(parts, data) # 对当前的阶段进行一个判断
if len(part_result) > 1: # 存在多个匹配值的时候取第一个匹配值
part_result = [part_result[0]]
if part_result == flag:
if part_result:
# 如果在就进行归类
f.append(data)
continue
else:
# 如果不在就交换flag 并且将存储当前单元的数据置空
flag = part_result
if f:
result.append(f)
f = [data, ]
# for i in result:
# print(i)
return result
def toc_extract(self):
"""
目录实体的抽取
"""
results = self.get_struction()
after = []
global detail
for one in results:
if results.index(one) == 0:
detail = self.title_detail(one)
else:
detail = self.content_detail(one)
if detail:
after.append(detail)
# for i in after:
# print(i)
def title_detail(self, sentence):
# ======================================================================================= 分开写为后面的段落结构划分
"""
主标题的抽取
:param sentence:
:return:
"""
result = []
for word in sentence:
mid = []
segment = jieba.lcut(word)
for one in segment:
# print(one)
if one not in self.stop_list:
mid.append(one)
result.append(' '.join(mid))
return result
def content_detail(self, sentence):
"""
子标题的抽取
:param sentence:
:return:
"""
result = []
for word in sentence:
word = re.sub(r'\d', '', word)
word = re.sub(r'\.', '', word)
segment = pseg.lcut(word)
# print(segment)
one = self.spo(segment)
if one:
result.append(one)
# print(result)
return result
def spo(self, segment):
"""
:param segment: 传入分词的列表
:return:
"""
# print(segment)
convert = [] # 存储转换后pair
for word, tags in segment: # 转换pair
# for i in one:
# t.append(i)
convert.append((word, tags))
stop_after = [] # 存储去除停用词的数组
for i in convert: # 去重停用词
if i[0] not in self.stop_list:
if i:
stop_after.append(i)
print(stop_after)
entity_chunk = ['t', 'v', 'n', 'vn'] # 向前的词
after = ['n', 'v', 'l'] # 最后的词
"""
从······看 替换成了- -
与 、 和 替换成了 -
"""
mid = [] # 返回的spo三元组
for one in range(len(stop_after) - 1, -1, -1):
if len(stop_after) == 2:
if stop_after[one][1] in after and stop_after[one - 1][1] in entity_chunk:
combine = stop_after[one - 1][0] + stop_after[one][0]
mid.append(combine)
break
else:
mid.append(stop_after[one])
if len(stop_after) == 3:
pass
# if stop_after[one][1] in after and after[one - 1][1] in entity_chunk:
# combine = after[one - 1][0] + after[one][0]
# mid.append(combine)
# break
# if one[i][1] in after and one[i - 1][1] in entity_chunk:
# combine = one[i - 1][0] + one[i][0]
# mid.append(combine)
# break
# for i in stop_after:
# # print(i)
# if len(stop_after) == 1:
# mid.append(i[0])
# break
# if len(stop_after) == 2:
# mid.append((stop_after[0][0]+stop_after[1][0]))
# break
# if len(stop_after) == 3:
# # if i[1] in after:
# # if stop_after[stop_after.index(i) - 1][1] in entity_chunk:
# # print(stop_after[stop_after.index(i) + 1])
# # store = stop_after[stop_after.index(i) + 1][0] + i[0]
# store = stop_after[1][0] + stop_after[2][0]
# mid.append((stop_after[0][0], ' ', store))
# break
# if len(stop_after) > 4:
# mid.append(stop_after)
# break
# if len(stop_after) == 4:
# break
# if i[0] == '-':
# mid.append(stop_after)
# break
print(f"我是mid：{mid}")
return mid
def start(self):
# for i in range(7):
self.get_stop_word()
self.toc_extract()
self.index += 1
if __name__ == '__main__':
toc = Toc()
toc.start()