Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying
This work addresses the need for more efficient and scalable machine learning systems in natural language processing by reducing reliance on labeled data, though it appears incremental as it builds on existing lifelong learning paradigms.
The paper tackles the problem of reducing manual data annotation in lifelong learning for sentiment classification by enabling knowledge accumulation and reuse across tasks, achieving performance comparable to or better than supervised learning with less labeled data and computational cost.
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 Naïve 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.