2006 TopicModelingBeyongBoW

• (Wallach, 2006) ⇒ Hanna M. Wallach. (2006). “Topic Modeling: beyond bag-of-words.” In: Proceedings of the 23rd ICML Conference (ICML 2006) doi:10.1145/1143844.1143967

Subject Headings: Topic Modeling Algorithm

Cited By

• Cited by ~48 http://scholar.google.com/scholar?cites=4759065448344938606&as_sdt=2000

Quotes

Abstract

• Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assumption, in which word order is ignored. Previously, these methods have not been combined. In this work, I explore a hierarchical generative probabilistic model that incorporates both n-gram statistics and latent topic variables by extending a unigram topic model to include properties of a hierarchical Dirichlet bigram language model. The model hyperparameters are inferred using a Gibbs EM algorithm. On two data sets, each of 150 documents, the new model exhibits better predictive accuracy than either a hierarchical Dirichlet bigram language model or a unigram topic model. Additionally, the inferred topics are less dominated by function words than are topics discovered using unigram statistics, potentially making them more meaningful.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 TopicModelingBeyongBoW				Topic Modeling: beyond bag-of-words			http://www.cs.umass.edu/~wallach/publications/wallach06beyond.pdf	10.1145/1143844.1143967