L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition
Emanuela Boros, Carlos-Emiliano González-Gallardo, Jose Moreno, Antoine Doucet
Abstract
This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings. We argue that using a language-specific and a multilingual language model could improve the performance of multilingual and mixed NER. Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts. Thus, we propose a straightforward technique for generating additional contexts with and without the presence of entities. Our findings suggest that, in our internal experimental setup, this approach is promising. However, we ranked above average for the high-resource languages and lower than average for low-resource and multilingual models.- Anthology ID:
- 2022.semeval-1.225
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1630–1638
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.225
- DOI:
- 10.18653/v1/2022.semeval-1.225
- Cite (ACL):
- Emanuela Boros, Carlos-Emiliano González-Gallardo, Jose Moreno, and Antoine Doucet. 2022. L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1630–1638, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition (Boros et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.225.pdf
- Data
- MultiCoNER