@inproceedings{gonzalez-gallardo-etal-2023-l3i,
    title = "{L}3{I}++ at {S}em{E}val-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition",
    author = "Gonzalez-Gallardo, Carlos-Emiliano  and
      Tran, Thi Hong Hanh  and
      Girdhar, Nancy  and
      Boros, Emanuela  and
      Moreno, Jose G.  and
      Doucet, Antoine",
    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-1.112",
    doi = "10.18653/v1/2023.semeval-1.112",
    pages = "807--814",
    abstract = "This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2023 Task 2, Multilingual Complex Named Entity Recognition (MultiCoNER II). Similar to MultiCoNER I, the task seeks to develop methods to detect semantic ambiguous and complex entities in short and low-context settings. However, MultiCoNER II adds a fine-grained entity taxonomy with over 30 entity types and corrupted data on the test partitions. We approach these complications following prompt-based learning as (1) a ranking problem using a seq2seq framework, and (2) an extractive question-answering task. Our findings show that even if prompting techniques have a similar recall to fine-tuned hierarchical language model-based encoder methods, precision tends to be more affected.",
}

This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2023 Task 2, Multilingual Complex Named Entity Recognition (MultiCoNER II). Similar to MultiCoNER I, the task seeks to develop methods to detect semantic ambiguous and complex entities in short and low-context settings. However, MultiCoNER II adds a fine-grained entity taxonomy with over 30 entity types and corrupted data on the test partitions. We approach these complications following prompt-based learning as (1) a ranking problem using a seq2seq framework, and (2) an extractive question-answering task. Our findings show that even if prompting techniques have a similar recall to fine-tuned hierarchical language model-based encoder methods, precision tends to be more affected.
Gonzalez-Gallardo, Carlos-Emiliano Tran, Thi Hong Hanh Girdhar, Nancy Boros, Emanuela Moreno, Jose G. Doucet, Antoine
L3I++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition