Dynamic Graph Transformer for Implicit Tag Recognition

Yi-Ting Liou, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
Textual information extraction is a typical research topic in the NLP community. Several NLP tasks such as named entity recognition and relation extraction between entities have been well-studied in previous work. However, few works pay their attention to the implicit information. For example, a financial news article mentioned “Apple Inc.” may be also related to Samsung, even though Samsung is not explicitly mentioned in this article. This work presents a novel dynamic graph transformer that distills the textual information and the entity relations on the fly. Experimental results confirm the effectiveness of our approach to implicit tag recognition.
Anthology ID:
2021.eacl-main.122
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1426–1431
Language:
URL:
https://aclanthology.org/2021.eacl-main.122
DOI:
10.18653/v1/2021.eacl-main.122
Bibkey:
Cite (ACL):
Yi-Ting Liou, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2021. Dynamic Graph Transformer for Implicit Tag Recognition. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1426–1431, Online. Association for Computational Linguistics.
Cite (Informal):
Dynamic Graph Transformer for Implicit Tag Recognition (Liou et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.122.pdf