细粒度实体分类

细粒度的实体分类，又叫 Entity Typing ，它可以将命名实体识别的结果进行更细粒度的分类，对应了OKNLP中的typing子模块。在OKNLP工具包中目前支持了基于 BERT 的算法，可以使用 oknlp.typing.get_by_name 来创建它。

在这篇文档中，我们主要将介绍细粒度实体分类工具的基本用法。

示例代码

>>> import oknlp
>>> model = oknlp.typing.get_by_name("bert")
>>> result = model([
...   ("我爱北京天安门", (2, 4)),
...   ("天安门上太阳升", (0, 3))
... ])
>>> result
[[('location', 0.7184367775917053), ('place', 0.8161897659301758), ('city', 0.622787356376648), ('country', 0.12566784024238586)], [('person', 0.22703033685684204), ('location', 0.20146676898002625), ('place', 0.45954859256744385)]]

输入 & 输出 说明

在细粒度实体分类任务的输入中，主要包含了两个部分：原句和实体位置。其中，实体位置是由一个左闭右开的区间来表示的。在示例代码中，第一句的实体为“北京”，第二句的实体为“天安门”。

输出为一系列可能的实体类别的概率。目前可能的实体类别如下所示：

实体列表

person	group	organization	location	entity	time	object	event	place	accident
actor	agency	airline	airplane	airport	animal	architect	army	art	artist
athlete	attack	author	award	biology	body_part	bridge	broadcast	broadcast_station	building
car	cemetery	chemistry	city	coach	company	computer	conflict	country	county
currency	degree	department	director	disease	doctor	drug	education	election	engineer
ethnic_group	facility	film	finance	food	game	geography	god	government	health
heritage	holiday	hospital	hotel	institution	instrument	internet	island	language	law
lawyer	league	legal	leisure	library	living_thing	mass_transit	medicine	military	mobile_phone
monarch	mountain	music	musician	music_school	natural_disaster	news	news_agency	other	park
planet	play	political_party	politician	product	programming_language	protest	province	rail	railway
religion	religious_leader	restaurant	road	scientific_method	ship	sign	society	software	soldier
spacecraft	sport	stage	stock_exchange	structure	subway	team	television_channel	television_network	television_program
theater	title	train	transit	transportation	treatment	water	weapon	website	writing