def _prepare(self):
""" 加载词典 """
cell_phone_location, zip_code_location, area_code_location = phone_location_loader()
self.zip_code_location = zip_code_location
self.area_code_location = area_code_location
self.cell_phone_location_trie = TrieTree()
for num, loc in cell_phone_location.items():
self.cell_phone_location_trie.add_node(num, loc)
self.cell_phone_pattern = re.compile(CELL_PHONE_CHECK_PATTERN)
self.landline_phone_pattern = re.compile(LANDLINE_PHONE_CHECK_PATTERN)
self.landline_area_code_pattern = re.compile(LANDLINE_PHONE_AREA_CODE_PATTERN)
# 运营商词典
telecom_operator = telecom_operator_loader()
self.telecom_operator_trie = TrieTree()
for num, loc in telecom_operator.items():
self.telecom_operator_trie.add_node(num, loc)
class LexiconNER(object):
''' 构建基于 Trie 词典的前向最大匹配算法，做实体识别。
Args:
entity_dicts(dict): 每个类型对应的实体词典
e.g.
{
'Person': ['张大山', '岳灵珊', '岳不群']
'Organization': ['成都市第一人民医院', '四川省水利局']
}
text: str 类型，被搜索的文本内容。
Return:
entity_list: 基于字 token 的实体列表
Examples:
>>> import jionlp as jio
>>> entity_dicts = {
'Person': ['张大山', '岳灵珊', '岳不群'],
'Organization': ['成都市第一人民医院', '四川省水利局']}
>>> lexicon_ner = jio.ner.LexiconNER(entity_dicts)
>>> text = '岳灵珊在四川省水利局上班。'
>>> result = lexicon_ner(text)
>>> print(result)
[{'type': 'Person', 'text': '岳灵珊', 'offset': [0, 3]},
{'type': 'Organization', 'text': '四川省水利局', 'offset': [4, 10]}]
'''
def __init__(self, entity_dicts):
''' 初始化构建词典 Trie 树 '''
self.trie_tree_obj = TrieTree()
for typing, entity_list in entity_dicts.items():
self.trie_tree_obj.build_trie_tree(entity_list, typing)
def __call__(self, text):
'''
标注数据，给定一个文本字符串，标注出所有的数据
Args:
text: 给定的文本 str 格式
Return:
entity_list: 标注的实体列表数据
'''
record_list = list() # 输出最终结果
i = 0
text_length = len(text)
while i < text_length:
pointer_orig = text[i:self.trie_tree_obj.depth + i]
pointer = pointer_orig.lower()
step, typing = self.trie_tree_obj.search(pointer)
if typing is not None:
record = {
'type': typing,
'text': pointer_orig[0:step],
'offset': [i, step + i]
}
record_list.append(record)
i += step
return record_list
def __init__(self, entity_dicts):
''' 初始化构建词典 Trie 树 '''
self.trie_tree_obj = TrieTree()
for typing, entity_list in entity_dicts.items():
self.trie_tree_obj.build_trie_tree(entity_list, typing)
class PhoneLocation(object):
""" 对于给定的电话号码，返回其归属地、区号、运营商等信息。
该方法与 jio.extract_phone_number 配合使用。
Args:
text(str): 电话号码文本。若输入为 jio.extract_phone_number 返回的结果，效果更佳。
注意，仅输入电话号码文本，如 "86-17309729105"、"13499013052"、"021 60128421" 等，
而 "81203432" 这样的电话号码则没有对应的归属地。
若输入 "343981217799212723" 这样的文本，会造成误识别，须首先从中识别电话号码，再进行
归属地、区号、运营商的识别
Returns:
dict: 该电话号码的类型，归属地，手机运营商
Examples:
>>> import jionlp as jio
>>> text = '联系电话：13288568202. (021)32830431'
>>> num_list = jio.extract_phone_number(text)
>>> print(num_list)
>>> res = [jio.phone_location(item['text']) for item in num_list]
>>> print(res)
# [{'text': '13288568202', 'offset': (5, 16), 'type': 'cell_phone'},
{'text': '(021)32830431', 'offset': (18, 31), 'type': 'landline_phone'}]
# {'number': '(021)32830431', 'province': '上海', 'city': '上海', 'type': 'landline_phone'}
# {'number': '13288568202', 'province': '广东', 'city': '揭阳',
'type': 'cell_phone', 'operator': '中国联通'}
"""
def __init__(self):
self.cell_phone_location_trie = None
def _prepare(self):
""" 加载词典 """
cell_phone_location, zip_code_location, area_code_location = phone_location_loader(
)
self.zip_code_location = zip_code_location
self.area_code_location = area_code_location
self.cell_phone_location_trie = TrieTree()
for num, loc in cell_phone_location.items():
self.cell_phone_location_trie.add_node(num, loc)
self.cell_phone_pattern = re.compile(CELL_PHONE_CHECK_PATTERN)
self.landline_phone_pattern = re.compile(LANDLINE_PHONE_CHECK_PATTERN)
self.landline_area_code_pattern = re.compile(
LANDLINE_PHONE_AREA_CODE_PATTERN)
# 运营商词典
telecom_operator = telecom_operator_loader()
self.telecom_operator_trie = TrieTree()
for num, loc in telecom_operator.items():
self.telecom_operator_trie.add_node(num, loc)
def __call__(self, text):
""" 输入一段电话号码文本，返回其结果 """
if self.cell_phone_location_trie is None:
self._prepare()
res = self.cell_phone_pattern.search(text)
if res is not None: # 匹配至手机号码
cell_phone_number = res.group()
first_seven = cell_phone_number[:7]
_, location = self.cell_phone_location_trie.search(first_seven)
province, city = location.split(' ')
# print(province, city)
_, operator = self.telecom_operator_trie.search(
cell_phone_number[:4])
return {
'number': text,
'province': province,
'city': city,
'type': 'cell_phone',
'operator': operator
}
res = self.landline_phone_pattern.search(text)
if res is not None: # 匹配至固话号码
# 抽取固话号码的区号
res = self.landline_area_code_pattern.search(text)
if res is not None:
area_code = res.group(1)
province, city = self.area_code_location.get(area_code,
' ').split(' ')
if province == '':
province, city = None, None
return {
'number': text,
'province': province,
'city': city,
'type': 'landline_phone'
}
else:
return {
'number': text,
'province': None,
'city': None,
'type': 'landline_phone'
}
return {
'number': text,
'province': None,
'city': None,
'type': 'unknown'
}
def landline_phone_location(self, phone_num):
""" 检索固定电话号码城市区号并返回，即已知输入是固话号码 """
if self.cell_phone_location_trie is None:
self._prepare()
# 抽取固话号码的区号
res = self.landline_area_code_pattern.search(phone_num)
if res is not None:
area_code = res.group(1)
province, city = self.area_code_location.get(area_code,
' ').split(' ')
if province == '':
province, city = None, None
return {
'number': phone_num,
'province': province,
'city': city,
'type': 'landline_phone'
}
else:
return {
'number': phone_num,
'province': None,
'city': None,
'type': 'landline_phone'
}
def cell_phone_location(self, phone_num):
""" 检索手机号码城市区号并返回，即已知输入是手机号 """
if self.cell_phone_location_trie is None:
self._prepare()
res = self.cell_phone_pattern.search(phone_num)
cell_phone_number = res.group()
first_seven = cell_phone_number[:7]
_, location = self.cell_phone_location_trie.search(first_seven)
province, city = location.split(' ')
_, operator = self.telecom_operator_trie.search(cell_phone_number[:4])
return {
'number': phone_num,
'province': province,
'city': city,
'type': 'cell_phone',
'operator': operator
}