DANGNT-SGU at SemEval-2022 Task 11: Using Pre-trained Language Model for Complex Named Entity Recognition

Dang Nguyen, Huy Khac Nguyen Huynh


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
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.203.pdf