Named entity recognition and relation classification are key stages for extracting information from unstructured text. Several natural language processing applications utilize the two tasks, such as information retrieval, knowledge graph construction and completion, question answering and other domain-specific applications, such as biomedical data mining. We present a survey of recent approaches in the two tasks with focus on few-shot learning approaches. Our work compares the main approaches followed in the two paradigms. Additionally, we report the latest metric scores in the two tasks with a structured analysis that considers the results in the few-shot learning scope.

翻译：命名实体识别与关系分类是从非结构化文本中提取信息的关键步骤。诸多自然语言处理应用均需利用这两项任务，例如信息检索、知识图谱构建与补全、问答系统，以及生物医学数据挖掘等特定领域应用。本文针对这两项任务中近期提出的方法进行了综述，重点关注基于小样本学习的方法。本研究对比了这两种范式所采用的主要技术路线。此外，我们报告了这两项任务的最新指标分数，并基于考虑小样本学习范畴内结果的结构化分析展开论述。