Abstract
This paper proposes an innovative approach in managing project-related documents, project domain analysis, and recommendation of open areas from current project document pool. Using keyterm extraction technique, documents are tagged under appropriate categories and subcategories for better management of project documents. Hence, this tagged document serves as a reference for the students who are planning to take up new projects. The system generates various reports for statistical analysis of projects carried out in each research domain. These statistics benefit users to get an overview of the trends of project works done over the past few years. There are also reports illustrating the number of open areas over respective academic years. The open areas are identified and listed for the students. This novel approach would help the students who are seeking new project. Our system helps the students, faculty, and other academicians to get involved in ongoing projects and also to obtain ideas in their respective research domain. We have modified the stemming method in basic keyterm extraction algorithm (KEA) by adding Porter stemmer rather than Lovins stemming method, and our experimental results confirm that our modified keyterm extraction method outperforms the KEA method while tagging English documents.
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Acknowledgements
We would like to express our sincere gratitude to the faculty of Department of Computer Science and Applications of Amrita Vishwa Vidyapeetham, Amritapuri, for providing help and guidance.
Our sincere thanks to Dr. M. R. Kaimal, Chairman, Computer Science Department, Amrita Vishwa Vidyapeetham, Amritapuri, for his prompt support.
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Thushara, M.G., Sreeremya, S.A., Smitha, S. (2018). KEA-Based Document Tagging for Project Recommendation and Analysis. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 708. Springer, Singapore. https://doi.org/10.1007/978-981-10-8636-6_30
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DOI: https://doi.org/10.1007/978-981-10-8636-6_30
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