A novel end-to-end neural network for simultaneous filtering of task-unrelated named entities and fine-grained typing of task-related named entities

作者：

Highlights：

• Task related and unrelated entities have very different properties.

• Simultaneously recognizing all entities is challenging.

• A novel end-to-end neural network is developed to classify all entities.

• An improved RBF classifier applies closed decision boundary for all the entity typing.

• Mention–mention relation strengthens the link of task related and unrelated entities.

摘要

•Task related and unrelated entities have very different properties.•Simultaneously recognizing all entities is challenging.•A novel end-to-end neural network is developed to classify all entities.•An improved RBF classifier applies closed decision boundary for all the entity typing.•Mention–mention relation strengthens the link of task related and unrelated entities.

论文关键词：Named entity typing,Fine-grained,End-to-end model,Improved radial function,Pipeline

论文评审过程：Received 4 May 2021, Revised 7 April 2022, Accepted 1 May 2022, Available online 18 May 2022, Version of Record 23 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117498