mlsanityData to 7 Oct 2026

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

arXiv 2203.03546 ↗First posted 13 Feb 2022cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 13 Feb 2022.

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24 of 38 references.

References · 24

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12 more
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Citing papers per year: 2023: 1.

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  1. 2023Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australiacited by 0