Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks

This paper proposes Anchor-Region Networks (ARNs), a sequence-to-nuggets architecture for nested mention detection which first identifies anchor words of all mentions, and then recognizes the mention boundaries for each anchor word by exploiting regular phrase structures.

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-toend 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.