Unveiling Strengths and Weaknesses of NLP Systems Based on a Rich Evaluation Corpus: The Case of NER in French

Alice Millour, Yoann Dupont, Karen Fort, Liam Duignan

Abstract
Named Entity Recognition (NER) is an applicative task for which annotation schemes vary. To compare the performance of systems which tagsets differ in precision and coverage, it is necessary to assess (i) the comparability of their annotation schemes and (ii) the individual adequacy of the latter to a common annotation scheme. What is more, and given the lack of robustness of some tools towards textual variation, we cannot expect an evaluation led on an homogeneous corpus with low-coverage to provide a reliable prediction of the actual tools performance. To tackle both these limitations in evaluation, we provide a gold corpus for French covering 6 textual genres and annotated with a rich tagset that enables comparison with multiple annotation schemes. We use the flexibility of this gold corpus to provide both: (i) an individual evaluation of four heterogeneous NER systems on their target tagsets, (ii) a comparison of their performance on a common scheme. This rich evaluation framework enables a fair comparison of NER systems across textual genres and annotation schemes.
Anthology ID:
2024.lrec-main.1495
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
17217–17224
Language:
URL:
https://aclanthology.org/2024.lrec-main.1495
DOI:
Bibkey:
Cite (ACL):
Alice Millour, Yoann Dupont, Karen Fort, and Liam Duignan. 2024. Unveiling Strengths and Weaknesses of NLP Systems Based on a Rich Evaluation Corpus: The Case of NER in French. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17217–17224, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Unveiling Strengths and Weaknesses of NLP Systems Based on a Rich Evaluation Corpus: The Case of NER in French (Millour et al., LREC-COLING 2024)
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