Dynamic graph transformer for implicit tag recognition
Journal
EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Pages
1426-1431
Date Issued
2021
Author(s)
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. ? 2021 Association for Computational Linguistics
Subjects
Natural language processing systems; Dynamic graph; Financial news; Implicit informations; Named entity recognition; On the flies; Relation extraction; Research topics; Textual information; Computational linguistics
Type
conference paper