Latent semantic indexing: a probabilistic analysis
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Computational Methods for Intelligent Information Access
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A semidiscrete matrix decomposition for latent semantic indexing information retrieval
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Inferring Demographic Attributes of Anonymus Internet Users
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Topic-based mixture language modelling
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Term-list translation using mono-lingual word co-occurrence vectors
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Anchor text mining for translation of Web queries: A transitive translation approach
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CubeSVD: a novel approach to personalized Web search
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A comparative evaluation of data-driven models in translation selection of machine translation
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Latent semantic information in maximum entropy language models for conversational speech recognition
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Variable latent semantic indexing
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Web object indexing using domain knowledge
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Knowledge-free induction of morphology using latent semantic analysis
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A framework for understanding latent semantic indexing (LSI) performance
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MUDABlue: an automatic categorization system for open source repositories
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Using HMM to learn user browsing patterns for focused web crawling
Data & Knowledge Engineering - Special issue: WIDM 2004
Demographic prediction based on user's browsing behavior
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Cache efficient bidiagonalization using BLAS 2.5 operators
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Methodological Review: Empirical distributional semantics: Methods and biomedical applications
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SenseClusters - finding clusters that represent word senses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Semantic density analysis: comparing word meaning across time and phonetic space
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
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Block Householder computation of sparse matrix singular values
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Implementation techniques for large-scale latent semantic indexing applications
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Column subset selection via sparse approximation of SVD
Theoretical Computer Science
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SVDPACKC comprises four numerical (iterative) methods for computing the singular value decomposition (SVD) of large sparse matrices using ANSI C. This software package implements Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left- and right-singular vectors) for large sparse matrices. The package has been ported to a variety of machines ranging from supercomputers to workstations: CRAY Y-MP, IBM RS/6000-550, DEC 5000-100, HP 9000-750, SPARCstation 2, and Macintosh II/fx. This document {\it (i)} explains each algorithm in some detail, {\it (ii)} explains the input parameters for each program, {\it (iii)} explains how to compile/execute each program, and {\it (iv)} illustrates the performance of each method when we compute lower rank approximations to sparse {\it term-document} matrices from information retrieval applications. A user-friendly software interface to the package for UNIX-based systems and the Macintosh II/fx is also described.