Probabilistic Retrieval of Full-Text Document Collections Using Logistic Regression

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This project develops and tests new probabilistic approaches to text retrieval. Using the statistical technique of logistic regression, documents are ranked in order of estimated probability of relevance with respect to a query. The methods are subjected to rigorous performance experiments with the collections of documents and queries of the TREC (Text REtrieval Conference) series of conferences sponsored by NIST (National Institute of Standards and Technology) and DARPA (Defense Advanced Research Projects Agency). Specifically the project investigates logistic regression retrieval in the following areas: (1) comparison of different logistic regression retrieval models; (2) new theoretical models which combine intelligent Boolean filtering with logistic regression; and (3) application of the models to Chinese and Spanish language retrieval. This project will advance the progress in modern text and document retrieval by developing sound theoretical models of the retrieval process, models which achieve high performance in experimental tests on millions of documents. The research will contribute to understanding of themechanisms of multilingual retrieval by applying its methodologies to queries and document collections in Chinese and Spanish.