L3i++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

L3i++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition

Résumé

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.

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Dates et versions

hal-04676751 , version 1 (23-08-2024)

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  • HAL Id : hal-04676751 , version 1

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Carlos-Emiliano González-Gallardo, Tran Thi Hong Hanh, Nancy Girdhar, Emanuela Boros, Jose G. Moreno, et al.. L3i++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition. 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Jun 2023, New Mexico, United States. ⟨hal-04676751⟩
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