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Return to Session IVa: Formal models - 2
We present a probabilistic model for a document corpus that combines many of the desirable features of previous models. The model is called "GaP" for Gamma-Poisson, the distributions of the first and last random variable. GaP is a factor model, that is it gives an approximate factorization of the document-term matrix into a product of matrices Ë and X. These factors have strictly non-negative terms. GaP is a generative probabilistic model that assigns finite probabilities to documents in a corpus. It can be computed with an efficient and simple EM recurrence. For a suitable choice of parameters, the GaP factorization maximizes independence between the factors. So it can be used as an independent-component algorithm adapted to document data. The form of the GaP model is empirically as well as analytically motivated. It gives very accurate results as a probabilistic model (measured via perplexity) and as a retrieval model. The GaP model projects documents and terms into a low-dimensional space of "themes," and models texts as "passages" of terms on the same theme.
@inproceedings{1009016, author = {John Canny}, title = {GaP: a factor model for discrete data}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {122--129}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009016}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
