Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction
Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. However, while maintaining an efficient exhaustive search on spans, the importance of syntactic features is not taken int...
Main Authors: | Haiyang Zhang, Guanqun Zhang, Yue Ma |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-02-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/4/1480 |
Similar Items
-
Joint Entity-Relation Extraction via Improved Graph Attention Networks
by: Qinghan Lai, et al.
Published: (2020-10-01) -
A Joint Learning Model to Extract Entities and Relations for Chinese Literature Based on Self-Attention
by: Huang, G.-Y, et al.
Published: (2022) -
Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military
by: Fei Liao, et al.
Published: (2019-08-01) -
A Text-Generated Method to Joint Extraction of Entities and Relations
by: Haihong E, et al.
Published: (2019-09-01) -
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-Aware Attention Using Latent Entity Typing
by: Joohong Lee, et al.
Published: (2019-06-01)