Probabilistic Modeling of Text Data: A Review

• Activity Number: 73
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Shibasish Dasgupta*+
• Companies: University of Florida at Gainesville
• Keywords: probabilistic inference ; text classification ; information retrieval ; document generalization

Abstract

The management of large and growing collections of information is a central goal of modern statistical science. Data repositories of texts have become widely accessible, thus necessitating good methods of retrieval, organization, and exploration. Probabilistic models have been paramount to these tasks, used in settings such as text classification, information retrieval, text segmentation, information extraction etc.

These methods entail two stages: (1) Estimate or compute the posterior distribution of the parameters of a probabilistic model from a collection of text; & (2) For new documents, answer the question at hand (e.g., classification, retrieval) via probabilistic inference.

The goal of such modeling is document generalization. Given a new document, how is it similar to the previously seen documents? Where does it fit within them? What can one predict about it? Efficiently answering such questions is the focus of the statistical analysis of document collections. This talk will consider the problem of modeling text corpora.