- Computing and Information Systems - Research Publications
Computing and Information Systems - Research Publications
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ItemQuery expansion using a collection dependent probabilistic latent semantic thesaurusPark, LAF ; Ramamohanarao, K ; Zhou, ZH ; Li, H ; Yang, Q (SPRINGER-VERLAG BERLIN, 2007)
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ItemKernel latent semantic analysis using an information retrieval based kernelPark, LAF ; Ramamohanarao, K (ACM, 2009-12-01)
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ItemThe Sensitivity of Latent Dirichlet Allocation for Information RetrievalPark, LAF ; Ramamohanarao, K ; Buntine, W ; Grobelnik, M ; Mladenic, D ; ShaweTaylor, J (SPRINGER-VERLAG BERLIN, 2009)
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ItemBroadening vector space schemes for improving the quality of information retrievalRamamohanarao, K ; Park, LAF ; Zhang, Y ; Tanaka, K ; Yu, JX ; Wang, S ; Li, M (SPRINGER-VERLAG BERLIN, 2005)
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ItemMining web multi-resolution community-based popularity for information retrievalPark, LAF ; Ramamohanarao, K (ACM, 2007-12-01)
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ItemPersonalized PageRank for web page prediction based on access time-length and frequencyGuo, YZ ; Rarnamohanarao, K ; Park, LAF ; Haas, LL ; Kacprzyk, J ; Motwani, R ; Broder, A ; Ho, H (IEEE COMPUTER SOC, 2007)
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ItemA novel document ranking method using the discrete cosine transformPark, LAF ; Palaniswami, M ; Ramamohanarao, K (IEEE COMPUTER SOC, 2005-01)We propose a new Spectral text retrieval method using the Discrete Cosine Transform (DCT). By taking advantage of the properties of the DCT and by employing the fast query and compression techniques found in vector space methods (VSM), we show that we can process queries as fast as VSM and achieve a much higher precision.
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ItemAn Analysis of Latent Semantic Term Self-CorrelationPark, LAF ; Ramamohanarao, K (ASSOC COMPUTING MACHINERY, 2009)Latent semantic analysis (LSA) is a generalized vector space method that uses dimension reduction to generate term correlations for use during the information retrieval process. We hypothesized that even though the dimension reduction establishes correlations between terms, the dimension reduction is causing a degradation in the correlation of a term to itself (self-correlation). In this article, we have proven that there is a direct relationship to the size of the LSA dimension reduction and the LSA self-correlation. We have also shown that by altering the LSA term self-correlations we gain a substantial increase in precision, while also reducing the computation required during the information retrieval process.
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ItemEfficient storage and retrieval of probabilistic latent semantic information for information retrievalPark, LAF ; Ramamohanarao, K (SPRINGER, 2009-01-01)Probabilistic latent semantic analysis (PLSA) is a method for computing term and document relationships from a document set. The probabilistic latent semantic index (PLSI) has been used to store PLSA information, but unfortunately the PLSI uses excessive storage space relative to a simple term frequency index, which causes lengthy query times. To overcome the storage and speed problems of PLSI, we introduce the probabilistic latent semantic thesaurus (PLST); an efficient and effective method of storing the PLSA information. We show that through methods such as document thresholding and term pruning, we are able to maintain the high precision results found using PLSA while using a very small percent (0.15%) of the storage space of PLSI.