Visualizing Topics with Multi-Word Expressions TeX Export

@article{citeulike:5084329, abstract = {We describe a new method for visualizing topics, the distributions over terms that are automatically extracted from large text corpora using latent variable models. Our method finds significant \$n\$-grams related to a topic, which are then used to help understand and interpret the underlying distribution. Compared with the usual visualization, which simply lists the most probable topical terms, the multi-word expressions provide a better intuitive impression for what a topic is "about." Our approach is based on a language model of arbitrary length expressions, for which we develop a new methodology based on nested permutation tests to find significant phrases. We show that this method outperforms the more standard use of \$\chi^2\$ and likelihood ratio tests. We illustrate the topic presentations on corpora of scientific abstracts and news articles.}, archivePrefix = {arXiv}, author = {Blei, David M. and Lafferty, John D.}, citeulike-article-id = {5084329}, citeulike-linkout-0 = {http://arxiv.org/abs/0907.1013}, citeulike-linkout-1 = {http://arxiv.org/pdf/0907.1013}, day = {6}, eprint = {0907.1013}, keywords = {2009, model, multi-word, topic, visualizing}, month = {Jul}, posted-at = {2009-08-15 06:13:07}, priority = {5}, title = {Visualizing Topics with Multi-Word Expressions}, url = {http://arxiv.org/abs/0907.1013}, year = {2009} }

TY - JOUR ID - citeulike:5084329 L3 - citeulike-article-id:5084329 N2 - We describe a new method for visualizing topics, the distributions over terms that are automatically extracted from large text corpora using latent variable models. Our method finds significant $n$-grams related to a topic, which are then used to help understand and interpret the underlying distribution. Compared with the usual visualization, which simply lists the most probable topical terms, the multi-word expressions provide a better intuitive impression for what a topic is "about." Our approach is based on a language model of arbitrary length expressions, for which we develop a new methodology based on nested permutation tests to find significant phrases. We show that this method outperforms the more standard use of $\chi^2$ and likelihood ratio tests. We illustrate the topic presentations on corpora of scientific abstracts and news articles. TI - Visualizing Topics with Multi-Word Expressions KW - 2009 KW - model KW - multi-word KW - topic KW - visualizing AU - Blei, David AU - Lafferty, John PY - 2009/Jul/06/ UR - http://arxiv.org/abs/0907.1013 ER -