  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.
