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

• URL: http://arxiv.org/abs/2107.09429v1
• Date: Tue, 20 Jul 2021 11:44:36 GMT
• Title: BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledge
• Authors: Huiqiang Jiang, Guoxin Wang, Weile Chen, Chengxi Zhang, B\"orje F. Karlsson
• Abstract summary: 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. 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.
• Score: 1.5149438988761574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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