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Lifelong machine learning is a novel machine learning paradigm which cancontinually accumulate knowledge during learning. The knowledge extracting andreusing abilities enable the lifelong machine learning to solve the relatedproblems. The traditional approaches like Na\"ive Bayes and some neural networkbased approaches only aim to achieve the best performance upon a single task.Unlike them, the lifelong machine learning in this paper focuses on how toaccumulate knowledge during learning and leverage them for further tasks.Meanwhile, the demand for labelled data for training also is significantlydecreased with the knowledge reusing. This paper suggests that the aim of thelifelong learning is to use less labelled data and computational cost toachieve the performance as well as or even better than the supervised learning.

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