LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition

• URL: http://arxiv.org/abs/2203.03546v1
• Date: Sun, 13 Feb 2022 05:46:14 GMT
• Title: LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition
• Authors: Ngoc Minh Lai
• Abstract summary: 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. Our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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