@inproceedings{ginn-khamov-2023-ginn,
    title = "Ginn-Khamov at {S}em{E}val-2023 Task 6, Subtask {B}: Legal Named Entities Extraction for Heterogenous Documents",
    author = "Ginn, Michael  and
      Khamov, Roman",
    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://aclanthology.org/2023.semeval-1.57",
    doi = "10.18653/v1/2023.semeval-1.57",
    pages = "420--425",
    abstract = "This paper describes our submission to SemEval-2023 Task 6, Subtask B, a shared task on performing Named Entity Recognition in legal documents for specific legal entity types. Documents are divided into the preamble and judgement texts, and certain entity types should only be tagged in one of the two text sections. To address this challenge, our team proposes a token classification model that is augmented with information about the document type, which achieves greater performance than the non-augmented system.",
}

This paper describes our submission to SemEval-2023 Task 6, Subtask B, a shared task on performing Named Entity Recognition in legal documents for specific legal entity types. Documents are divided into the preamble and judgement texts, and certain entity types should only be tagged in one of the two text sections. To address this challenge, our team proposes a token classification model that is augmented with information about the document type, which achieves greater performance than the non-augmented system.
Ginn, Michael Khamov, Roman
Ginn-Khamov at SemEval-2023 Task 6, Subtask B: Legal Named Entities Extraction for Heterogenous Documents