Journal
NEURAL NETWORKS
Volume 142, Issue -, Pages 340-350Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2021.02.019
Keywords
Named entity recognition; Sequence labeling; Multi-task learning
Funding
- National Natural Science Foundation of China [62076100]
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities, SCUT [2017ZD048, D2182480]
- Science and Technology Programs of Guangzhou [201704030076, 201802010027, 201902010046]
- Hong Kong Research Grants Council [C1031-18G]
- Science and Technology Planning Project of Guangdong Province [2020B0101100002]
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Named entity recognition (NER) is crucial in NLP tasks. We propose a neural multi-task model for extracting nested entities, which performs better and more efficiently compared to feature-based models.
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. (C) 2021 Published by Elsevier Ltd.
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