LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition
This work addresses complex named entity recognition for NLP researchers, but it is incremental as it applies an existing method to a new dataset.
The paper tackled English named entity recognition in a SemEval-2022 task, achieving a macro F1 score of 72.50% and ranking 12th out of 30 teams with a Transformer-based baseline.
Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English track of SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective Transformer-based baseline for the task. Despite its simplicity, our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams. Our system achieved a macro F1 score of 72.50% on the held-out test set. We have also explored a data augmentation approach using entity linking. While the approach does not improve the final performance, we also discuss it in this paper.