A novel end-to-end neural network for simultaneous filtering of task-unrelated named entities and fine-grained typing of task-related named entities
Recently, one emerging problem in Named Entity Typing (NET) is the fine-grained classification of task-related entities co-existing with task-unrelated entities. The traditional pipeline framework decomposes this problem into two sub-tasks. The first sub-task filters out the task-unrelated entities,...
Main Authors: | Li, Qi, Mao, Kezhi, Li, Pengfei, Xu, Yuecong, Lo, Edmond Yat Man |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/162082 |
Similar Items
-
Converse attention knowledge transfer for low-resource named entity recognition
by: Lyu, Shengfei, et al.
Published: (2024) -
Linking fine-grained locations in user comments
by: Han, Jialong, et al.
Published: (2019) -
Generalized AutoNLP model for name entity recognition task
by: Wong, Yung Shen
Published: (2022) -
Marshall–Olkin power-law distributions in length-frequency of entities
by: Zhong, Xiaoshi, et al.
Published: (2023) -
Named entity recognition approaches
by: Mansouri, Alireza, et al.
Published: (2008)