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View all- Sun ZLi X(2023)Named Entity Recognition Model Based on Feature FusionInformation10.3390/info1402013314:2(133)Online publication date: 17-Feb-2023
ESN-NER: entity storage network using attention mechanism for chinese NER
Article No.: 41, Pages 1 - 8
Abstract
Chinese named entity recognition (NER) is more difficult than it in English because of the lack of nature delimiters. First, Chinese NER requires word segmentation, but word-based segmentation will generate errors due to the different granularity of the word segmentation tools. Second, most NER models heavily rely on local linguistic features, but the scope of influence provided by local linguistic features is limited, so sometimes the model will give different results to the same entity in different sentences. To address the above problems, we propose the Entity Storage Network Model called ESN Model for Chinese NER, which is a character-based model to avoid word segmentation errors. Specifically, we design an entity storage layer in this model to extract and store the entity information as a local linguistic feature, and design a position feature which is generated by four flags to enhance the learning of boundary. Then we incorporate the attention mechanism to extend the scope of the local linguistic features. The experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art models in Chinese NER task.
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Index Terms
- ESN-NER: entity storage network using attention mechanism for chinese NER
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Association for Computing Machinery
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Published: 19 December 2019
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- Research-article
Funding Sources
- the Guangxi Universities Young and Middle-aged Teacher Basic Ability Enhancement Project
- Guangxi Innovation-Driven Development Project
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province
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AIIPCC '19
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- ASciE
AIIPCC '19: 2019 International Conference on Artificial Intelligence, Information Processing and Cloud Computing
December 19 - 21, 2019
Sanya, China
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AIIPCC '19 Paper Acceptance Rate 78 of 211 submissions, 37%;
Overall Acceptance Rate 78 of 211 submissions, 37%
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View all- Sun ZLi X(2023)Named Entity Recognition Model Based on Feature FusionInformation10.3390/info1402013314:2(133)Online publication date: 17-Feb-2023
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