Text Classification Using Lifelong Machine Learning
This paper proposes a novel lifelong machine learning model for text classification. The proposed model tries to solve problems as humans do i.e. it learns small and simple problems, retains the knowledge learnt from those problems, mines the useful information from the stored knowledge and reuses the extracted knowledge to learn future problems. The proposed approach adopts rule based learning classifier systems and a new encoding scheme is proposed to identify building units of knowledge which can be reused for future learning. The fitter building units from the learning system trained against small problems of text classification domain are extracted and utilized in high dimensional social media text classification problems to achieve scalable learning. The experimental results show that proposed continuous learning approach successfully solves complex high dimensional problems by reusing the previously learned fitter building blocks of knowledge.
KeywordsLifelong learning Code fragments Text classification
The corresponding author is Xing Jin. This work is supported by SKLSDE-2016ZX-11, NSFC program (No.61472022, 61421003), and partly by the Beijing Advanced Innovation Center for Big Data and Brain Computing.
- 1.Chen, Z., Ma, N., Liu, B.: Lifelong learning for sentiment classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, pp. 750–756 (2015)Google Scholar
- 5.Chen, Z., Liu, B.: Mining topics in documents: standing on the shoulders of big data. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1116–1125. ACM, New York (2014)Google Scholar
- 6.Chen, Z., Liu, B.: Lifelong Machine Learning. Morgan & Claypool, San Francisco (2016)Google Scholar
- 7.Daniel, L.S., Yang, Q., Li, L.: Lifelong machine learning systems: beyond learning algorithms. In: AAAI Spring Symposium: Lifelong Machine Learning, vol. SS-13-05 of AAAI Technical Report. AAAI (2013)Google Scholar
- 8.Arif, M.H., Li, J., Iqbal, M., Liu, K.: Sentiment analysis and spam detection using learning classifier systems. In: Soft Computing (2017). doi: 10.1007/s00500-017-2729-x
- 9.Arif, M.H., Li, J., Iqbal, M., Peng, H.: Optimizing XCSR for text classification. In: Proceedings of the IEEE Symposium on Service-Oriented System Engineering, pp. 86–95. IEEE Press, San Francisco (2017)Google Scholar
- 12.Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal text. J. Artif. Int. Res. 50(1), 723–762 (2014)Google Scholar
- 13.Andrew, L.M., Raymond, E.D., Peter, T.P., Dan, H., Andrew, Y.N., Christopher, P.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, pp. 142–150 (2011)Google Scholar