Abstract
In this paper, we describe our proposed method for the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER). The goal of this task is to locate and classify named entities in unstructured short complex texts in 11 different languages.After training a variety of contextual language models on the NER dataset, we used an ensemble strategy based on a majority vote to finalize our model. We evaluated our proposed approach on the multilingual NER dataset at SemEval-2022. The ensemble model provided consistent improvements against the individual models on the multilingual track, achieving a macro F1 performance of 65.2%. However, our results were significantly outperformed by the top ranking systems, achieving thus a baseline performance.- Anthology ID:
- 2022.semeval-1.212
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1543–1548
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.212
- DOI:
- 10.18653/v1/2022.semeval-1.212
- Cite (ACL):
- Hossein Rouhizadeh and Douglas Teodoro. 2022. DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1543–1548, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- DS4DH at SemEval-2022 Task 11: Multilingual Named Entity Recognition Using an Ensemble of Transformer-based Language Models (Rouhizadeh & Teodoro, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.semeval-1.212.pdf
- Data
- MultiCoNER