论文标题
LMN在Semeval-2022任务11：基于变压器的英语命名实体识别系统
LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition
论文作者
论文摘要
处理复杂和模棱两可的命名实体是一个具有挑战性的研究问题，但尚未受到自然语言处理社区的足够关注。在这篇简短的论文中，我们介绍了参与Semeval-2022任务的英语曲目11：多语言综合体名称实体识别。受到验证的变压器语言模型的最新进展的启发，我们为任务提出了一个简单而有效的基于变压器的基线。尽管它很简单，但我们提出的方法在排行榜中表现出竞争成果，因为我们在30支球队中排名12。我们的系统在持有测试集中获得了72.50％的宏F1分数。我们还使用实体链接探索了数据增强方法。尽管该方法无法改善最终表现，但我们也在本文中进行了讨论。
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.
