Nguyen, Ngoc Vu and Nguyen, Thi Lan and Nguyen, Thi Cam Van and Tran, Mai Vu and Nguyen, Tri Thanh and Ha, Quang Thuy (2019) Improving Named Entity Recognition in Vietnamese Texts by a Character-Level Deep Lifelong Learning Model. Vietnam J. Computer Science, 6 (4). pp. 471-487. ISSN 2196-8888
Full text not available from this repository.Abstract
Named entity recognition (NER) is a fundamental task which affects the performance of its dependent task, e.g. machine translation. Lifelong machine learning (LML) is a continuous learning process, in which the knowledge base accumulated from previous tasks will be used to improve future learning tasks having few samples. Since there are a few studies on LML based on deep neural networks for NER, especially in Vietnamese, we propose a lifelong learning model based on deep learning with a CRFs layer, named DeepLML–NER, for NER in Vietnamese texts. DeepLML–NER includes an algorithm to extract the knowledge of “prefix-features” of named entities in previous domains. Then the model uses the knowledge in the knowledge base to solve the current NER task. Preprocessing and model parameter tuning are also investigated to improve the performance. The effect of the model was demonstrated by in-domain and cross-domain experiments, achieving promising results.
| Item Type: | Article |
|---|---|
| Subjects: | Information Technology (IT) |
| Divisions: | Faculty of Information Technology (FIT) |
| Depositing User: | Hà Quang Thụy |
| Date Deposited: | 26 Nov 2019 07:47 |
| Last Modified: | 26 Nov 2019 07:47 |
| URI: | http://eprints.uet.vnu.edu.vn/eprints/id/eprint/3590 |
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