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Multi-task learning with mutual learning for joint sentiment classification and topic detection
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Gui, Lin, Jia, Leng, Zhou, Jiyun, Xu, Ruifeng and He, Yulan (2020) Multi-task learning with mutual learning for joint sentiment classification and topic detection. IEEE Transactions on Knowledge and Data Engineering . doi:10.1109/TKDE.2020.2999489
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WRAP-multi-task-learning-mutual-learning-joint-sentiment-classification-topic-detection-Lin-2020.pdf - Accepted Version - Requires a PDF viewer. Download (3764Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TKDE.2020.2999489
Abstract
Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modelling. On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classification accuracy. On the other hand, the word-level attention vectors obtained during the learning of sentiment classifiers could carry word-level polarity information and can be used to guide the discovery of topics in topic modelling. This paper proposes a multi-task learning framework which jointly learns a sentiment classifier and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in the classifier through mutual learning. Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classification accuracy and topic modelling evaluation results including perplexity and topic coherence measures. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.
| Item Type: | Journal Article | |||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||||||||||||||
| Divisions: | Faculty of Science > Computer Science | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Artificial intelligence, Probabilities, Machine learning, Natural language processing (Computer science), Computational linguistics | |||||||||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Knowledge and Data Engineering | |||||||||||||||||||||
| Publisher: | IEEE Computer Society | |||||||||||||||||||||
| ISSN: | 1041-4347 | |||||||||||||||||||||
| Official Date: | 9 June 2020 | |||||||||||||||||||||
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| DOI: | 10.1109/TKDE.2020.2999489 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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