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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.eacl-main.122.pdf