Abstract
Automatic text categorization (ATC) is a prominent research area within Information retrieval. Through this paper a classification model for ATC in multi-label domain is discussed. We are proposing a new multi label text classification model for assigning more relevant set of categories to every input text document. Our model is greatly influenced by graph based framework and Semi supervised learning. We demonstrate the effectiveness of our model using Enron, Slashdot, Bibtex and RCV1 datasets. We also compare performance of our model with few popular existing supervised techniques. Our experimental results indicate that the use of Semi Supervised Learning in multi label text classification greatly improves the decision making capability of classifier.
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Dharmadhikari, S.C., Ingle, M., Kulkarni, P. (2014). Semi Supervised Learning Based Text Classification Model for Multi Label Paradigm. In: Das, V.V., Elkafrawy, P. (eds) Signal Processing and Information Technology. SPIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-11629-7_26
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DOI: https://doi.org/10.1007/978-3-319-11629-7_26
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