As a fundamental natural language processing task and one of core knowledge
extraction techniques, named entity recognition (NER) is widely used to extract
information from texts for downstream tasks. Nested NER is a branch of NER in
which the named entities (NEs) are nested with each other. However, most of the
previous studies on nested NER usually apply linear structure to model the
nested NEs which are actually accommodated in a hierarchical structure. Thus in
order to address this mismatch, this work models the full nested NEs in a
sentence as a holistic structure, then we propose a holistic structure parsing
algorithm to disclose the entire NEs once for all. Besides, there is no
research on applying corpus-level information to NER currently. To make up for
the loss of this information, we introduce Point-wise Mutual Information (PMI)
and other frequency features from corpus-aware statistics for even better
performance by holistic modeling from sentence-level to corpus-level.
Experiments show that our model yields promising results on widely-used
benchmarks which approach or even achieve state-of-the-art. Further empirical
studies show that our proposed corpus-aware features can substantially improve
NER domain adaptation, which demonstrates the surprising advantage of our
proposed corpus-level holistic structure modeling.

本文介绍了一种针对命名实体识别中嵌套实体的一种新方法 —— 全局结构分析和点互信息统计，提高了该方法的性能及领域适应能力。