LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER
Shilpa Chatterjee, Leo Evenss, Pramit Bhattacharyya, Joydeep Mondal
Abstract
This study introduces the system submitted to the SemEval 2022 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) by the LSJSP team. We propose FTBC, a FastText-based framework with pre-trained Bert for NER tasks with complex entities and over a noisy dataset. Our system achieves an average of 58.27% F1 score (fine-grained) and 75.79% F1 score (coarse-grained) across all languages. FTBC outperforms the baseline BERT-CRF model on all 12 monolingual tracks.- Anthology ID:
- 2023.semeval-1.174
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1254–1259
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.174
- DOI:
- 10.18653/v1/2023.semeval-1.174
- Cite (ACL):
- Shilpa Chatterjee, Leo Evenss, Pramit Bhattacharyya, and Joydeep Mondal. 2023. LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1254–1259, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER (Chatterjee et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.semeval-1.174.pdf