@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T L3I++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition
%A Gonzalez-Gallardo, Carlos-Emiliano
%A Tran, Thi Hong Hanh
%A Girdhar, Nancy
%A Boros, Emanuela
%A Moreno, Jose G.
%A Doucet, Antoine
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gonzalez-gallardo-etal-2023-l3i
%X 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.
%R 10.18653/v1/2023.semeval-1.112
%U https://aclanthology.org/2023.semeval-1.112
%U https://doi.org/10.18653/v1/2023.semeval-1.112
%P 807-814
Markdown (Informal)
[L3I++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition](https://aclanthology.org/2023.semeval-1.112) (Gonzalez-Gallardo et al., SemEval 2023)
ACL