Abstract
In this paper, we describe a system that we built to participate in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition, specifically the track Mono-lingual in English. To construct this system, we used Pre-trained Language Models (PLMs). Especially, the Pre-trained Model base on BERT is applied for the task of recognizing named entities by fine-tuning method. We performed the evaluation on two test datasets of the shared task: the Practice Phase and the Evaluation Phase of the competition.- Anthology ID:
- 2022.semeval-1.203
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1483–1487
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.203
- DOI:
- 10.18653/v1/2022.semeval-1.203
- Cite (ACL):
- Dang Nguyen and Huy Khac Nguyen Huynh. 2022. DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1483–1487, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition (Nguyen & Huynh, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2022.semeval-1.203.pdf