论文标题
Semeval-2022任务11：复杂NER的基于变压器的体系结构
Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER
论文作者
论文摘要
我们调查了英语复杂NER的任务。由于文本结构的语义歧义和这种实体在普遍文献中发生的罕见性，因此该任务是不平凡的。使用预先训练的语言模型（例如BERT），我们在此任务上获得了竞争性能。我们定性地分析了此任务的多个体系结构的性能。我们所有的模型都能够大大优于基线。我们表现最好的模型将基线F1得分击败了9％以上。
We investigate the task of complex NER for the English language. The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature. Using pre-trained language models such as BERT, we obtain a competitive performance on this task. We qualitatively analyze the performance of multiple architectures for this task. All our models are able to outperform the baseline by a significant margin. Our best performing model beats the baseline F1-score by over 9%.
