Topic modeling: beyond bag-of-words

  • Authors:
  • Hanna M. Wallach

  • Affiliations:
  • University of Cambridge, Cambridge, UK

  • Venue:
  • ICML '06 Proceedings of the 23rd international conference on Machine learning
  • Year:
  • 2006

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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.