Hong, Xianbin 
ORCID: 0000-0003-1678-0948, Pal, Gautam ORCID: 0000-0002-2594-9699, Guan, Sheng-Uei, Wong, Prudence ORCID: 0000-0001-7935-7245, Liu, Dawei, Man, Ka Lok and Huang, Xin
(2019)
Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less
Manual Data Annotation and More Self-Studying.
Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference.
pp. 87-93.
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Semi_Unsupervised_Lifelong_Learning_forSentiment_Classification__Less_Manual_DataAnnotation_and_More_Self_Studying.pdf - Author Accepted Manuscript Download (604kB) | Preview |
Abstract
Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Na\"ive Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | cs.CL, cs.CL, cs.AI |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 27 Sep 2019 07:28 |
| Last Modified: | 19 Jan 2023 00:25 |
| DOI: | 10.1145/3341069.3342992 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3056073 |
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