def named_entity_recognition(self, sent, standard_name=False):
"""
利用pyhanlp的命名实体识别，找到句子中的（人名，地名，机构名）三种实体。harvesttext会预先链接已知实体
:param sent:
:param standard_name:
:return: 发现的命名实体信息，字典 {实体名: 实体类型}
"""
from pyhanlp import HanLP, JClass
if not self.hanlp_prepared:
self.hanlp_prepare()
self.standard_name = standard_name
entities_info = self.entity_linking(sent)
sent2 = self.decoref(sent, entities_info)
StandardTokenizer = JClass("com.hankcs.hanlp.tokenizer.StandardTokenizer")
StandardTokenizer.SEGMENT.enableAllNamedEntityRecognize(True)
entity_type_dict = {}
try:
for x in StandardTokenizer.segment(sent2):
# 三种前缀代表：人名（nr），地名（ns），机构名（nt）
tag0 = str(x.nature)
if tag0.startswith("nr"):
entity_type_dict[x.word] = "人名"
elif tag0.startswith("ns"):
entity_type_dict[x.word] = "地名"
elif tag0.startswith("nt"):
entity_type_dict[x.word] = "机构名"
elif tag0.startswith("nz"):
entity_type_dict[x.word] = "其他专名"
except:
pass
return entity_type_dict
def named_entity_recognition(self, sent, standard_name=False, return_posseg=False):
'''利用pyhanlp的命名实体识别，找到句子中的（人名，地名，机构名，其他专名）实体。harvesttext会预先链接已知实体
:param sent: string, 文本
:param standard_name: bool, 是否把连接到的已登录转化为标准名
:param return_posseg: bool, 是否返回包括命名实体识别的，带词性分词结果
:param book: bool, 预先识别
:return: entity_type_dict: 发现的命名实体信息，字典 {实体名: 实体类型}
(return_posseg=True时) possegs: list of (单词, 词性)
'''
from pyhanlp import HanLP, JClass
if not self.hanlp_prepared:
self.hanlp_prepare()
self.standard_name = standard_name
entities_info = self.entity_linking(sent)
sent2 = self.decoref(sent, entities_info)
StandardTokenizer = JClass("com.hankcs.hanlp.tokenizer.StandardTokenizer")
StandardTokenizer.SEGMENT.enableAllNamedEntityRecognize(True)
entity_type_dict = {}
try:
possegs = []
for x in StandardTokenizer.segment(sent2):
# 三种前缀代表：人名（nr），地名（ns），机构名（nt）
tag0 = str(x.nature)
if tag0.startswith("nr"):
entity_type_dict[x.word] = "人名"
elif tag0.startswith("ns"):
entity_type_dict[x.word] = "地名"
elif tag0.startswith("nt"):
entity_type_dict[x.word] = "机构名"
elif tag0.startswith("nz"):
entity_type_dict[x.word] = "其他专名"
possegs.append((x.word, tag0))
except:
pass
if return_posseg:
return entity_type_dict, possegs
else:
return entity_type_dict
def hanlp_cut(text):
tokenizer = JClass("com.hankcs.hanlp.tokenizer.NLPTokenizer")
return " ".join([term.word for term in tokenizer.segment(text)])