@inproceedings{oba-etal-2022-entity,
    title = "Entity Embedding Completion for Wide-Coverage Entity Disambiguation",
    author = "Oba, Daisuke  and
      Yamada, Ikuya  and
      Yoshinaga, Naoki  and
      Toyoda, Masashi",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.findings-emnlp.472/",
    doi = "10.18653/v1/2022.findings-emnlp.472",
    pages = "6333--6344",
    abstract = "Entity disambiguation (ED) is typically solved by learning to classify a given mention into one of the entities in the model{'}s entity vocabulary by referring to their embeddings. However, this approach cannot address mentions of entities that are not covered by the entity vocabulary. Aiming to enhance the applicability of ED models, we propose a method of extending a state-of-the-art ED model by dynamically computing embeddings of out-of-vocabulary entities. Specifically, our method computes embeddings from entity descriptions and mention contexts. Experiments with standard benchmark datasets show that the extended model performs comparable to or better than existing models whose entity embeddings are trained for all candidate entities as well as embedding-free models. We release our source code and model checkpoints at https://github.com/studio-ousia/steel."
}