While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drastically increasing difficulties in classifying different entity categories in the few-shot setting. Although prior work has briefly discussed nested structures in the context of few-shot learning, to our best knowledge, this paper is the first one specifically dedicated to studying the few-shot nested NER task. Leveraging contextual dependency to distinguish nested entities, we propose a Biaffine-based Contrastive Learning (BCL) framework. We first design a Biaffine span representation module for learning the contextual span dependency representation for each entity span rather than only learning its semantic representation. We then merge these two representations by the residual connection to distinguish nested entities. Finally, we build a contrastive learning framework to adjust the representation distribution for larger margin boundaries and more generalized domain transfer learning ability. We conducted experimental studies on three English, German, and Russian nested NER datasets. The results show that the BCL outperformed three baseline models on the 1-shot and 5-shot tasks in terms of F1 score.
翻译:虽然命名实体识别(NER)是一项广泛研究的任务,但只有少数标签数据的实体的推论一直具有挑战性,特别是对有巢状结构的实体而言。与平板实体、实体及其巢状实体相比,更可能具有相似的语义特征说明,在将不同实体类别分类方面大大增加了困难。虽然先前的工作在少见的学习中简要讨论了嵌套结构,但据我们的最佳知识,本文是第一个专门研究微小的嵌套式净化任务的实体。我们利用背景依赖性来区分嵌巢实体,我们提议了一个基于双亚麻碱的反向学习框架。我们首先设计了一个用于学习每个实体背景依赖性说明的双亚麻宽代表模块,而不是仅仅学习其语义说明。我们随后将这两个说明合并为剩余联系,以区分嵌套式实体。最后,我们建立了一个对比性学习框架,以调整代表性分布,以更大的边界和更普遍的域域转移能力。我们对三个英国、德国和俄罗斯的嵌套式NER数据学习能力进行了实验性研究。我们首先设计了一个用于学习基准1和分级标准1。结果显示BCL标准第五个基准。