Hierarchical topic models and the nested Chinese restaurant process

@misc{citeulike:1772388, abstract = {We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting -- which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested Chinese restaurant process. This nonparametric prior allows arbitrarily large branching factors and readily accommodates growing data collections. We build a hierarchical topic model by combining...}, author = {Blei, D. and Gri, T. and Jordan, M. and Tenenbaum, J. }, booktitle = {Neural Information Processing Systems(NIPS)}, citeulike-article-id = {1772388}, keywords = {lda, topic\_detection}, posted-at = {2008-05-05 23:28:33}, priority = {2}, title = {Hierarchical topic models and the nested Chinese restaurant process}, url = {http://citeseer.ist.psu.edu/630071.html}, year = {2003} }

TY - GEN ID - citeulike:1772388 L3 - citeulike-article-id:1772388 TI - Hierarchical topic models and the nested Chinese restaurant process T2 - Neural Information Processing Systems(NIPS) N2 - We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting -- which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested Chinese restaurant process. This nonparametric prior allows arbitrarily large branching factors and readily accommodates growing data collections. We build a hierarchical topic model by combining... KW - lda KW - topic_detection AU - Blei, D AU - Gri, T AU - Jordan, M AU - Tenenbaum, J PY - 2003/// UR - http://citeseer.ist.psu.edu/630071.html ER -