Reading List for Computational Models Standing Offer

Comparison of Models

Lee, M.D., Pincombe, B.M., & Welsh, M.B. (2005). An empirical evaluation of models of text document similarity.In B.G. Bara, L.W. Barsalou & M. Bucciarelli, (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society, pp. 1254-1259. Mahwah, NJ: Erlbaum. PDF

Vector Space Model

G. Salton , A. Wong , C. S. Yang, A vector space model for automatic indexing, Communications of the ACM, v.18 n.11, p.613-620, Nov. 1975. PDF


Kintsch, W., McNamara, D., Dennis, S. & Landauer, T. (2006). Handbook of Latent Semantic Analysis. Mahwah:NJ. Lawrence Erlbaum Associates. (especially Chapter 2). PDF

Topics Model

Griffiths, T. L., & Steyvers, M. (2002). A probabilistic approach to semantic representation. In C. D. Schunn & W. D. Gray (Eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society: Lawrence Erlbaum Associates. PDF

Latent Dirichlet Allocation, David Blei, Andrew Y. Ng and Michael Jordan. Journal of Machine Learning Research, 3:993-1022, 2003. PDF


Isbell, C. L. and Viola, P.: Restucturing sparse high dimensional data for effective retrieval, In: Advances in Neural Injbrmation Processing Systems 11, 1998, pp. 480486. PDF

Sparse ICA

A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, Sparse ICA for blind separation of transmitted and reflected images, Intl. Journal of Imaging Science and Technology (IJIST), 15(1), pp. 84-91, 2005. PDF

Nonnegative Matrix Factorization

Xu, W., Liu, X., & Gong, Y. (2003). Document clustering based on non-negative matrix factorization. SIGIR, Toronto, Canada. PDF

*Ding, C. Li, T. & Peng, W. (2006). Nonnegative matrix factorization and probabilistic latent semantic indexing: Equivalence, Chi-square statistic and a hybrid method. AAAI PDF

*Cai, D., He, X., & Han, J. (2005). Document clustering using locality preserving indexing. IEEE Transactions on knowledge and data engineering, 17 (12), 1624-1637. PDF

Syntagmatic Paradigmatic Model

Dennis, S. (2004). An unsupervised method for the extraction of propositional information from text. Proceedings of the National Academy of Sciences. 101, 5206-5213. PDF

Mørup, M., Hansen, L. K.,Arnfred, S. M. (2006). Algorithms for Sparse Higher Order Non-negative Matrix Factorization (HONMF). PDF

* Hazan, T., Polak, S. & Shashua, A. (2005). Sparse Image Coding using a 3D Non-negative Tensor Factorization. International Conference on Computer Vision (ICCV) Beijing, China, October. PDF