Dynamic Graph Transformer for Implicit Tag Recognition
Yi-Ting Liou | Chung-Chi Chen | Hen-Hsen Huang | Hsin-Hsi Chen
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
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.