@inproceedings{zhao-etal-2023-legal,
    title = "Legal{\_}try at {S}em{E}val-2023 Task 6: Voting Heterogeneous Models for Entities identification in Legal Documents",
    author = "Zhao, Junzhe  and
      Wang, Yingxi  and
      Rusnachenko, Nicolay  and
      Liang, Huizhi",
    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-1.178",
    doi = "10.18653/v1/2023.semeval-1.178",
    pages = "1282--1286",
    abstract = "Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that involves identifying and categorizing named entities. The result annotation makes unstructured natural texts applicable for other NLP tasks, including information retrieval, question answering, and machine translation. NER is also essential in legal as an initial stage in extracting relevant entities. However, legal texts contain domain-specific named entities, such as applicants, defendants, courts, statutes, and articles. The latter makes standard named entity recognizers incompatible with legal documents. This paper proposes an approach combining multiple models{'} results via a voting mechanism for unique entity identification in legal texts. This endeavor focuses on extracting legal named entities, and the specific assignment (task B) is to create a legal NER system for unique entity annotation in legal documents. The results of our experiments and system implementation are published in \url{https://github.com/SuperEDG/Legal_Project}.",
}

Named Entity Recognition (NER) is a subtask of Natural Language Processing (NLP) that involves identifying and categorizing named entities. The result annotation makes unstructured natural texts applicable for other NLP tasks, including information retrieval, question answering, and machine translation. NER is also essential in legal as an initial stage in extracting relevant entities. However, legal texts contain domain-specific named entities, such as applicants, defendants, courts, statutes, and articles. The latter makes standard named entity recognizers incompatible with legal documents. This paper proposes an approach combining multiple models{'} results via a voting mechanism for unique entity identification in legal texts. This endeavor focuses on extracting legal named entities, and the specific assignment (task B) is to create a legal NER system for unique entity annotation in legal documents. The results of our experiments and system implementation are published in \url{https://github.com/SuperEDG/Legal_Project}.
Zhao, Junzhe Wang, Yingxi Rusnachenko, Nicolay Liang, Huizhi
Legal_try at SemEval-2023 Task 6: Voting Heterogeneous Models for Entities identification in Legal Documents