Abstract
Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recognizing nested entity mentions. In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i.e., although a mention can nest other mentions, they will not share the same head word. Specifically, we propose Anchor-Region Networks (ARNs), a sequence-to-nuggets architecture for nested mention detection. ARNs first identify anchor words (i.e., possible head words) of all mentions, and then recognize the mention boundaries for each anchor word by exploiting regular phrase structures. Furthermore, we also design Bag Loss, an objective function which can train ARNs in an end-to-end manner without using any anchor word annotation. Experiments show that ARNs achieve the state-of-the-art performance on three standard nested entity mention detection benchmarks.
Abstract (translated)
基于序列标记的NER方法限制了每个词至多属于一个被提及的实体,这将在识别嵌套实体被提及时面临一个严重的问题。在本文中,我们建议通过建模和利用实体提及的头驱动短语结构来解决这个问题,即尽管一个提及可以嵌套其他提及,但它们不会共享同一个头词。具体地说,我们提出了锚区域网络(arns),这是一种面向掘金的序列结构,用于嵌套的提及检测。ARN首先识别所有提及的锚定词(即可能的首词),然后利用规则短语结构识别每个锚定词的提及边界。此外,我们还设计了一个目标函数bag loss,它可以在不使用任何锚定词注释的情况下,以端到端的方式训练ARN。实验表明,ARN在三个标准的嵌套实体提及检测基准上实现了最先进的性能。
URL
https://arxiv.org/abs/1906.03783