@inproceedings{liou-etal-2021-dynamic,
    title = "Dynamic Graph Transformer for Implicit Tag Recognition",
    author = "Liou, Yi-Ting  and
      Chen, Chung-Chi  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.eacl-main.122/",
    doi = "10.18653/v1/2021.eacl-main.122",
    pages = "1426--1431",
    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."
}