Creating Your Own LSA Spaces

Handbook of Latent Semantic Analysis

Volumn , Issue , 2007, Pages 71-85

  Quesada, Jose ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85145544749     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203936399-8     Document Type: Chapter

References (23)

1. Blei, D., Ng, A. Y., and Jordan, M. I. (2001). Latent dirichlet allocation. In T. K. Leen, T. G. Dietterich, andV. Trestp (Eds.), Advances in Neural Information Processing Systems 13, (pp. 601-608). Cambridge, MA: MIT Press.
2. Cox, T.F., and Cox, M.A. A. (2001). Multidimensional scaling. London:ChapmanandHall.
3. Dumais, S. (1991). Improving the retrieval of information from external sources. Behavior Research Methods, Instruments and Computers, 23(2), 229-236.
4. Dumais, S. (2003). Data-driven approaches to information access. Cognitive Science, 27(3), 491-524.
5. Giles, J. T., Wo, L., and Berry, M.W. (2003). GTP (general text parser) software for text mining. In H. Bozdogan (Ed.), Statistical data mining and knowledge discovery (pp. 455-471). Boca Raton, FL: CRC press.
6. Griffiths, T. L., and Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228-5235.
7. Harman, D. (1986). An experimental study of the factors important in document ranking. In Association for computing machinery conference on research and development in information retrieval. New York: Association for Computing Machinery.
8. Hofmann, T. (1999). Probabilistic latent semantic indexing. Proceedings of 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 50-57.
9. Koenker, R., and Ng, P. (2004). SparseM (R-package; Version 0.54) [Computer software]. Vienna, Austria: The R foundation.
10. Landauer, T. K. (2002). On the computational basis of learning and cognition: Arguments from LSA. In N. Ross (Ed.), The psychology of learning and motivation (Vol. 41, pp. 43-84). New York: Academic Press.
11. Landauer, T. K., and Dumais, S. T. (1997). Asolution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211-240.
12. Larsen, R. M. (2000). Lanczos bidiagonalization with partial reorthogonalization (PROPACK;Version 2.1) [Computer program]. Avavilable from http://soi.stanford.edu/~rmunk/PROPACK
13. Lee, M. D. (2001). Determining the dimensionality of multidimensional scaling representations for cognitive modeling. Journal of Mathematical Psychology, 45(1), 149-166.
14. MathWorks. (2004). Matlab. Natick, MA: The MathWorks.
15. Minka, T. P. (2001). Automatic choice of dimensionality for PCA. In S. Becker, S. Thrun, andK. Obermayer, (Eds.), Advances in Neural Information Processing Systems 15 (pp. 598-604). Cambridge, MA: MIT Press.
16. Muller, J.-P. (2004). Tools for textual data anaylsis (R-package; Version 0.1.1) [Computer tools]. Available from http://wwwpeople.unil.ch/jean-pierre.mueller
17. Myers, D. G. (1995). Psychology (5th ed.). New York: Worth Publishers.
18. Parlett, B. N. (1998). The symmetric eigenvalue problem (2nd ed.). Philadelphia: SIAM.
19. Scott, P. (2004). Perl Medic:Transforming legacy code. Reading, MA:Addison-Wesley.
20. Team, R. D. C. (2004). R:Alanguage and environment for statistical computing (Version 2.0). Vienna, Austria: R Foundation for Statistical Computing.
21. Wolfram Research, I. (2004). Mathematica (Version 5.0). Champaign, IL: Wolfram Research.
22. Zeimpekis, D., and Gallopoulos, E. (2004). Text to matrix generator (Version 3) [Computer toolbox]. Available from http://scgroup.hpclab.ceid.upatras.gr./scgroup/Projects/TMG
23. Zipf, G. K. (1949). Human behaviour and the principle of least-effort. Cambridge, MA: Addison-Wesley.