Abstract
Modern information retrieval activities are supported with software systems that facilitate the users’ information searching. Information retrieval systems are significantly improved in the past few decades. Now days, there are three types of retrieval models: Boolean, Vector Space and Probabilistic. In this study, we examined the vector space model where documents and queries are represented as vectors. We conducted a number of experiments on the indexing technique of the vector space model to quantitatively describe the effectiveness of the techniques using Lemur Toolkit. The result indicates that stop word removal and steaming techniques improve the quality of the index terms.
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Acknowledgement
This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2014M3C4A7030503).
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Shin, JH., Abebe, M., Yoo, C.J., Kim, S., Lee, J.H., Yoo, HK. (2017). Evaluating the Effectiveness of the Vector Space Retrieval Model Indexing. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_104
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DOI: https://doi.org/10.1007/978-981-10-3023-9_104
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