Liou Y.-TChen C.-CHuang H.-HHSIN-HSI CHEN2021-09-022021-09-022021https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106426695&partnerID=40&md5=7cd7a9915813ccd7dae71cb77ab4ec56https://scholars.lib.ntu.edu.tw/handle/123456789/581359Textual 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 LinguisticsNatural language processing systems; Dynamic graph; Financial news; Implicit informations; Named entity recognition; On the flies; Relation extraction; Research topics; Textual information; Computational linguisticsconference paper2-s2.0-85106426695