Abstract
Classification of text documents from a pool of huge collection of the same is performed usually on the basis of certain key terms present in the said documents that distinguish a particular document set from the universal set. Generally, these key terms are identified using some feature sets, which can be statistical, rule-based, linguistic, or hybrid in nature. This paper develops a simple technique based on Venn diagram to prioritize the different standard features available in the literature, which in turn reduces the dimension of the feature sets used for document classification.
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Acknowledgements
The authors are thankful to the Center for Microprocessor Applications for Training Education and Research (CMATER) of C.S.E. Dept., JU, for providing infrastructural facilities during progress of the work. The current work, reported here, has been partially funded by Technical Education Quality Improvement Programme Phase–II (TEQIP-II), Jadavpur University, Kolkata, India.
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Chakraborty, N., Mukherjee, S., Naskar, A.R., Malakar, S., Sarkar, R., Nasipuri, M. (2017). Venn Diagram-Based Feature Ranking Technique for Key Term Extraction. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_33
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DOI: https://doi.org/10.1007/978-981-10-3153-3_33
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