CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification
Keyu Pu, Hongyi Liu, Yixiao Yang, Jiangzhou Ji, Wenyi Lv, Yaohan He
Abstract
This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition. What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings. Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans. The basic pre-trained models we choose are XLM-RoBERTa and mT5. The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.- Anthology ID:
- 2022.semeval-1.221
- 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:
- 1603–1607
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.221
- DOI:
- 10.18653/v1/2022.semeval-1.221
- Cite (ACL):
- Keyu Pu, Hongyi Liu, Yixiao Yang, Jiangzhou Ji, Wenyi Lv, and Yaohan He. 2022. CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1603–1607, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification (Pu et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.semeval-1.221.pdf