@inproceedings{lovon-melgarejo-etal-2023-meerqat,
    title = "{MEERQAT}-{IRIT} at {S}em{E}val-2023 Task 2: Leveraging Contextualized Tag Descriptors for Multilingual Named Entity Recognition",
    author = "Lovon-Melgarejo, Jesus  and
      Moreno, Jose G.  and
      Besan{\c{c}}on, Romaric  and
      Ferret, Olivier  and
      Lechani, Lynda",
    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-1.121",
    doi = "10.18653/v1/2023.semeval-1.121",
    pages = "878--884",
    abstract = "This paper describes the system we submitted to the SemEval 2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II) in four monolingual tracks (English, Spanish, French, and Portuguese). Considering the low context setting and the fine-grained taxonomy presented in this task, we propose a system that leverages the language model representations using hand-crafted tag descriptors. We explored how integrating the contextualized representations of tag descriptors with a language model can help improve the model performance for this task. We performed our evaluations on the development and test sets used in the task for the Practice Phase and the Evaluation Phase respectively.",
}

This paper describes the system we submitted to the SemEval 2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II) in four monolingual tracks (English, Spanish, French, and Portuguese). Considering the low context setting and the fine-grained taxonomy presented in this task, we propose a system that leverages the language model representations using hand-crafted tag descriptors. We explored how integrating the contextualized representations of tag descriptors with a language model can help improve the model performance for this task. We performed our evaluations on the development and test sets used in the task for the Practice Phase and the Evaluation Phase respectively.
Lovon-Melgarejo, Jesus Moreno, Jose G. Besan{\c{c}}on, Romaric Ferret, Olivier Lechani, Lynda
MEERQAT-IRIT at SemEval-2023 Task 2: Leveraging Contextualized Tag Descriptors for Multilingual Named Entity Recognition