Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying



Hong, Xianbin ORCID: 0000-0003-1678-0948, Pal, Gautam, 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.

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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: 28 Nov 2019 09:23
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URI: http://livrepository.liverpool.ac.uk/id/eprint/3056073
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