Abstract
Lifelong machine learning is emerging in recent years thanks to its ability to use past knowledge for current problem. Lifelong topic modeling algorithms, such as LTM and AMC, are proposed and they are very useful. However, these algorithms focus on learning bias on the topic level not the domain level. This paper proposes a lifelong topic modeling method, which focuses on learning bias on the domain level based on a proposed domain closeness measure, and an application framework for multi-label classification on Vietnamese texts. Experimental results on three previously solved Vietnamese texts, and five different current Vietnamese text datasets in combination with different topic set sizes showed that our proposed method is better than AMC method for all cases.
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Ha, QT. et al. (2018). A New Lifelong Topic Modeling Method and Its Application to Vietnamese Text Multi-label Classification. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_19
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DOI: https://doi.org/10.1007/978-3-319-75417-8_19
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