Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model

Reducing high-dimensional data vectors to robust and interpretable lower-dimensional representa- tions has a long and successful history in data analysis, including recent innovations such as latent semantic indexing (LSI) (Deerwester et al, 1994) and latent Dirichlet allocation (LDA) (Blei, Ng, and Jordan, 2003). These types of techniques have found broad application in modeling of sparse high-dimensional count data such as the "bag of words" representations for documents or transaction data for Web and retail applications. Approaches such as LSI and LDA have both been shown to be useful for "object matching" in their respective latent spaces. In information retrieval for example, both a query and a set of documents can be represented in the LSI or topic latent spaces, and the documents can be ranked in terms of how well they match the query based on distance or similarity in the latent space. The mapping to latent space represents a generalization or abstraction away from the sparse set of observed words, to a "higher-level" semantic representation in the latent space. These abstractions in principle lead to better generalization on new data compared to inferences carried out directly in the original sparse high-dimensional space. The capability of these models to provide improved generalization has been demonstrated empirically in a number of studies (e.g., Deerwester et al 1994; Hofmann 1999; Canny 2004; Buntine et al, 2005). However, while this type of generalization is broadly useful in terms of inference and prediction, there are situations where one can over-generalize. Consider trying to match the following query to a historical archive of news articles: election campaign Camejo.