@inproceedings{verma-bergler-2023-clac,
title = "{CL}a{C} at {S}em{E}val-2023 Task 2: Comparing Span-Prediction and Sequence-Labeling Approaches for {NER}",
author = "Verma, Harsh and
Bergler, Sabine",
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-1.215",
doi = "10.18653/v1/2023.semeval-1.215",
pages = "1558--1561",
abstract = "This paper summarizes the CLaC submission for the MultiCoNER 2 task which concerns the recognition of complex, fine-grained named entities. We compare two popular approaches for NER, namely SequenceLabeling and Span Prediction. We find that our best Span Prediction system performs slightly better than our best Sequence Labeling system on test data. Moreover, we find that using the larger version of XLM RoBERTa significantly improves performance. Post-competition experiments show that Span Prediction and Sequence Labeling approaches improve when they use special input tokens ([s] and [/s]) of XLM-RoBERTa. The code for training all models, preprocessing, and post-processing is available at \url{https://github.com/harshshredding/semeval2023-multiconer-paper}.",
}
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<abstract>This paper summarizes the CLaC submission for the MultiCoNER 2 task which concerns the recognition of complex, fine-grained named entities. We compare two popular approaches for NER, namely SequenceLabeling and Span Prediction. We find that our best Span Prediction system performs slightly better than our best Sequence Labeling system on test data. Moreover, we find that using the larger version of XLM RoBERTa significantly improves performance. Post-competition experiments show that Span Prediction and Sequence Labeling approaches improve when they use special input tokens ([s] and [/s]) of XLM-RoBERTa. The code for training all models, preprocessing, and post-processing is available at https://github.com/harshshredding/semeval2023-multiconer-paper.</abstract>
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%0 Conference Proceedings
%T CLaC at SemEval-2023 Task 2: Comparing Span-Prediction and Sequence-Labeling Approaches for NER
%A Verma, Harsh
%A Bergler, Sabine
%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 verma-bergler-2023-clac
%X This paper summarizes the CLaC submission for the MultiCoNER 2 task which concerns the recognition of complex, fine-grained named entities. We compare two popular approaches for NER, namely SequenceLabeling and Span Prediction. We find that our best Span Prediction system performs slightly better than our best Sequence Labeling system on test data. Moreover, we find that using the larger version of XLM RoBERTa significantly improves performance. Post-competition experiments show that Span Prediction and Sequence Labeling approaches improve when they use special input tokens ([s] and [/s]) of XLM-RoBERTa. The code for training all models, preprocessing, and post-processing is available at https://github.com/harshshredding/semeval2023-multiconer-paper.
%R 10.18653/v1/2023.semeval-1.215
%U https://aclanthology.org/2023.semeval-1.215
%U https://doi.org/10.18653/v1/2023.semeval-1.215
%P 1558-1561
Markdown (Informal)
[CLaC at SemEval-2023 Task 2: Comparing Span-Prediction and Sequence-Labeling Approaches for NER](https://aclanthology.org/2023.semeval-1.215) (Verma & Bergler, SemEval 2023)
ACL