Candidate region aware nested named entity recognition. (October 2021)

• Record Type: Journal Article
• Authors: Jiang, Deng; Ren, Haopeng; Cai, Yi; Xu, Jingyun; Liu, Yanxia; Leung, Ho-fung
• Is Part Of: Neural networks. Volume 142(2021)
• Page Start: 340
• Page End: 350
• Publication Date: 2021-10
• Subjects: Named entity recognition -- Sequence labeling -- Multi-task learning; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.02.019
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Named entity recognition (NER) is crucial in various natural language processing (NLP) tasks. However, the nested entities which are common in practical corpus are often ignored in most of current NER models. To extract the nested entities, two categories of models (i.e., feature-based and neural network-based approaches) are proposed. However, the feature-based models suffer from the complicated feature engineering and often heavily rely on the external resources. Discarding the heavy feature engineering, recent neural network-based methods which treat the nested NER as a classification task are designed but still suffer from the heavy class imbalance issue and the high computational cost. To solve these problems, we propose a neural multi-task model with two modules: Binary Sequence Labeling and Candidate Region Classification to extract the nested entities. Extensive experiments are conducted on the public datasets. Comparing with recent neural network-based approaches, our proposed model achieves the better performance and obtains the higher efficiency.