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
Bibkey:
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)
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