@inproceedings{biales-2023-sab,
    title = "{SAB} at {S}em{E}val-2023 Task 2: Does Linguistic Information Aid in Named Entity Recognition?",
    author = "Biales, Siena",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.semeval-1.157/",
    doi = "10.18653/v1/2023.semeval-1.157",
    pages = "1131--1137",
    abstract = "This paper describes the submission to SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) by team SAB. This task aims to encourage growth in the field of Named Entity Recognition (NER) by focusing on complex and difficult categories of entities, in 12 different language tracks. The task of NER has historically shown the best results when a model incorporates an external knowledge base or gazetteer, however, less research has been applied to examining the effects of incorporating linguistic information into the model. In this task, we explored combining NER, part-of-speech (POS), and dependency relation labels into a multi-task model and report on the findings. We determine that the addition of POS and dependency relation information in this manner does not improve results."
}