用绑定模型提升嵌套术语识别，三赛道夺冠。
Methods for Recognizing Nested Terms
- 基于绑定模型，从非嵌套标注数据中识别嵌套术语。
- 在鲁术语评估竞赛三个赛道均取得最优成绩。
- 无需嵌套标注即可有效提取嵌套术语，适合资源有限场景。
本文介绍我们在鲁术语评估竞赛中针对嵌套术语抽取任务的参与情况。我们应用此前在嵌套命名实体识别中表现优异的Binder模型来提取嵌套术语，并在该竞赛的三个赛道中均取得了最佳结果。此外，我们还研究了一项新任务：从仅标注非嵌套术语的平铺训练数据中识别嵌套术语。实验表明，本文提出的多种方法在无需嵌套标注的情况下，仍能有效识别嵌套术语，具备实际可行性。
原文摘要 · Abstract (English)
In this paper, we describe our participation in the RuTermEval competition devoted to extracting nested terms. We apply the Binder model, which was previously successfully applied to the recognition of nested named entities, to extract nested terms. We obtained the best results of term recognition in all three tracks of the RuTermEval competition. In addition, we study the new task of recognition of nested terms from flat training data annotated with terms without nestedness. We can conclude that several approaches we proposed in this work are viable enough to retrieve nested terms effectively without nested labeling of them.
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