Abstract
This paper describes the method we submitted as the Janko team in the SemEval-2023 Task 2,Multilingual Complex Named Entity Recognition (MultiCoNER 2). We only participated in the Chinese track. In this paper, we implement the BERT-BiLSTM-RDrop model. We use the fine-tuned BERT models, take the output of BERT as the input of the BiLSTM network, and finally use R-Drop technology to optimize the loss function. Our submission achieved a macro-averaged F1 score of 0.579 on the testset.- Anthology ID:
- 2023.semeval-1.132
- 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:
- 958–962
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.132
- DOI:
- 10.18653/v1/2023.semeval-1.132
- Cite (ACL):
- Jiankuo Li, Zhengyi Guan, and Haiyan Ding. 2023. Janko at SemEval-2023 Task 2: Bidirectional LSTM Model Based on Pre-training for Chinese Named Entity Recognition. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 958–962, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Janko at SemEval-2023 Task 2: Bidirectional LSTM Model Based on Pre-training for Chinese Named Entity Recognition (Li et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.semeval-1.132.pdf