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 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
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.