On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition
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Published:2023-02-17
Issue:2
Volume:15
Page:79
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ISSN:1999-5903
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Container-title:Future Internet
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language:en
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Short-container-title:Future Internet
Author:
Mehmood Tahir12, Serina Ivan1ORCID, Lavelli Alberto2ORCID, Putelli Luca1, Gerevini Alfonso1
Affiliation:
1. Department of Information Engineering, University of Brescia, Via Branze 38, 25121 Brescia, Italy 2. NLP Research Group, Fondazione Bruno Kessler, Via Sommarive 18, 38123 Trento, Italy
Abstract
Biomedical named entity recognition (BioNER) is a preliminary task for many other tasks, e.g., relation extraction and semantic search. Extracting the text of interest from biomedical documents becomes more demanding as the availability of online data is increasing. Deep learning models have been adopted for biomedical named entity recognition (BioNER) as deep learning has been found very successful in many other tasks. Nevertheless, the complex structure of biomedical text data is still a challenging aspect for deep learning models. Limited annotated biomedical text data make it more difficult to train deep learning models with millions of trainable parameters. The single-task model, which focuses on learning a specific task, has issues in learning complex feature representations from a limited quantity of annotated data. Moreover, manually constructing annotated data is a time-consuming job. It is, therefore, vital to exploit other efficient ways to train deep learning models on the available annotated data. This work enhances the performance of the BioNER task by taking advantage of various knowledge transfer techniques: multitask learning and transfer learning. This work presents two multitask models (MTMs), which learn shared features and task-specific features by implementing the shared and task-specific layers. In addition, the presented trained MTM is also fine-tuned for each specific dataset to tailor it from a general features representation to a specialized features representation. The presented empirical results and statistical analysis from this work illustrate that the proposed techniques enhance significantly the performance of the corresponding single-task model (STM).
Subject
Computer Networks and Communications
Reference79 articles.
1. Mehmood, T., Gerevini, A.E., Lavelli, A., and Serina, I. (2020, January 16–18). Combining Multi-task Learning with Transfer Learning for Biomedical Named Entity Recognition. Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: 24th International Conference KES-2020, Virtual Event. 2. Mehmood, T., Gerevini, A., Lavelli, A., and Serina, I. (2019, January 19–22). Leveraging Multi-task Learning for Biomedical Named Entity Recognition. Proceedings of the AI*IA 2019—Advances in Artificial Intelligence—XVIIIth International Conference of the Italian Association for Artificial Intelligence, Rende, Italy. 3. Mehmood, T., Gerevini, A., Lavelli, A., and Serina, I. (2019, January 13–15). Multi-task Learning Applied to Biomedical Named Entity Recognition Task. Proceedings of the Sixth Italian Conference on Computational Linguistics, Bari, Italy. 4. Xu, M., Jiang, H., and Watcharawittayakul, S. (August, January 30). A Local Detection Approach for Named Entity Recognition and Mention Detection. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, BC, Canada. 5. Lin, Y.F., Tsai, T.H., Chou, W.C., Wu, K.P., Sung, T.Y., and Hsu, W.L. (2004, January 22). A maximum entropy approach to biomedical named entity recognition. Proceedings of the 4th International Conference on Data Mining in Bioinformatics, Seattle, WA, USA.
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