This paper presents a simple and effective approach in low-resource named entity recognition (NER) based on multi-hop dependency trigger. Dependency trigger refer to salient nodes relative to a entity in the dependency graph of a context sentence. Our main observation is that there often exists trigger which play an important role to recognize the location and type of entity in sentence. Previous research has used manual labelling of trigger. Our main contribution is to propose use a syntactic parser to automatically annotate trigger. Experiments on two English datasets (CONLL 2003 and BC5CDR) show that the proposed method is comparable to the previous trigger-based NER model.
翻译:本文件介绍了一种基于多点依赖触发的简单而有效的低资源命名实体识别方法(NER),其依据是多点依赖触发。在上下文句的依附图中,依赖触发指与一个实体有关的突出节点。我们的主要看法是,经常有触发点,在识别该句中实体的位置和类型方面起着重要作用。以前的研究使用了人工标记触发点。我们的主要贡献是建议使用合成分析器自动通知触发点。对两个英国数据集的实验(CONLL 2003和BC5CDR)表明,拟议的方法与先前的基于触发的NER模型相当。