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Title: Multi-task learning for sentiment analysis with hard-sharing and task recognition mechanisms
Authors: Zhang, Jian
Yan, Ke 
Mo, Yuchang
Keywords: Hard-sharing mechanism
Multi-task learning
Task recognition mechanism
Text classification
Issue Date: 12-May-2021
Publisher: MDPI AG
Citation: Zhang, Jian, Yan, Ke, Mo, Yuchang (2021-05-12). Multi-task learning for sentiment analysis with hard-sharing and task recognition mechanisms. Information (Switzerland) 12 (5) : 207. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: In the era of big data, multi-task learning has become one of the crucial technologies for sentiment analysis and classification. Most of the existing multi-task learning models for sentiment analysis are developed based on the soft-sharing mechanism that has less interference between different tasks than the hard-sharing mechanism. However, there are also fewer essential features that the model can extract with the soft-sharing method, resulting in unsatisfactory classification performance. In this paper, we propose a multi-task learning framework based on a hard-sharing mechanism for sentiment analysis in various fields. The hard-sharing mechanism is achieved by a shared layer to build the interrelationship among multiple tasks. Then, we design a task recognition mechanism to reduce the interference of the hard-shared feature space and also to enhance the correlation between multiple tasks. Experiments on two real-world sentiment classification datasets show that our approach achieves the best results and improves the classification accuracy over the existing methods significantly. The task recognition training process enables a unique representation of the features of different tasks in the shared feature space, providing a new solution reducing interference in the shared feature space for sentiment analysis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Information (Switzerland)
ISSN: 2078-2489
DOI: 10.3390/info12050207
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

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