论文标题
一个基于序列标记的框架，用于几次射击关系提取
A Sequence Tagging based Framework for Few-Shot Relation Extraction
论文作者
论文摘要
关系提取（RE）是指在输入文本中提取关系三元组。现有的基于神经工作的系统在很大程度上依赖于手动标记的培训数据，但是仍然有很多域中不存在足够标记的数据。受到基于距离的几弹性实体识别方法的启发，我们根据标记关节提取方法的序列标记了几个射击任务的定义，并为任务提出了一些弹出框架。此外，我们将两个实际的序列标记模型应用于我们的框架（称为少数TPLINKER和几杆Bitt），并在从公共数据集构建的两个少量RE任务上取得了可靠的结果。
Relation Extraction (RE) refers to extracting the relation triples in the input text. Existing neural work based systems for RE rely heavily on manually labeled training data, but there are still a lot of domains where sufficient labeled data does not exist. Inspired by the distance-based few-shot named entity recognition methods, we put forward the definition of the few-shot RE task based on the sequence tagging joint extraction approaches, and propose a few-shot RE framework for the task. Besides, we apply two actual sequence tagging models to our framework (called Few-shot TPLinker and Few-shot BiTT), and achieves solid results on two few-shot RE tasks constructed from a public dataset.
