Text-Based Information Retrieval Using Exponentiated Gradient Descent

The following investigates the use of single-neuron learning algo(cid: 173) rithms to improve the performance of text-retrieval systems that accept natural-language queries. A retrieval process is explained that transforms the natural-language query into the query syntax of a real retrieval system: the initial query is expanded using statis(cid: 173) tical and learning techniques and is then used for document ranking and binary classification. The results of experiments suggest that Kivinen and Warmuth's Exponentiated Gradient Descent learning algorithm works significantly better than previous approaches.

Authors

• Ron Papka
• James Callan
• Andrew Barto University of Massachusetts Amherst

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