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