  While named entity recognition (NER) is a key task in natural language
processing, most approaches only target flat entities, ignoring nested
structures which are common in many scenarios. Most existing nested NER methods
traverse all sub-sequences which is both expensive and inefficient, and also
don't well consider boundary knowledge which is significant for nested
entities. In this paper, we propose a joint entity mention detection and typing
model via prior boundary knowledge (BoningKnife) to better handle nested NER
extraction and recognition tasks. BoningKnife consists of two modules,
MentionTagger and TypeClassifier. MentionTagger better leverages boundary
knowledge beyond just entity start/end to improve the handling of nesting
levels and longer spans, while generating high quality mention candidates.
TypeClassifier utilizes a two-level attention mechanism to decouple different
nested level representations and better distinguish entity types. We jointly
train both modules sharing a common representation and a new dual-info
attention layer, which leads to improved representation focus on entity-related
information. Experiments over different datasets show that our approach
outperforms previous state of the art methods and achieves 86.41, 85.46, and
94.2 F1 scores on ACE2004, ACE2005, and NNE, respectively.
