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While named entity recognition (NER) is a key task in natural languageprocessing, most approaches only target flat entities, ignoring nestedstructures which are common in many scenarios. Most existing nested NER methodstraverse all sub-sequences which is both expensive and inefficient, and alsodon't well consider boundary knowledge which is significant for nestedentities. In this paper, we propose a joint entity mention detection and typingmodel via prior boundary knowledge (BoningKnife) to better handle nested NERextraction and recognition tasks. BoningKnife consists of two modules,MentionTagger and TypeClassifier. MentionTagger better leverages boundaryknowledge beyond just entity start/end to improve the handling of nestinglevels and longer spans, while generating high quality mention candidates.TypeClassifier utilizes a two-level attention mechanism to decouple differentnested level representations and better distinguish entity types. We jointlytrain both modules sharing a common representation and a new dual-infoattention layer, which leads to improved representation focus on entity-relatedinformation. Experiments over different datasets show that our approachoutperforms previous state of the art methods and achieves 86.41, 85.46, and94.2 F1 scores on ACE2004, ACE2005, and NNE, respectively.

BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledge