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NEURAL NETWORK APPLICATIONS FOR AUTOMATIC NEW TOPIC IDENTIFICATION ON EXCITE WEB SEARCH ENGINE DATA LOGS

H. Cenk ÷zmutlu hco@uludag.edu.tr Fatih «avdur fcavdur@uludag.edu.tr Seda ÷zmutlu seda@uludag.edu.tr Amanda Spink aspink@sis.pitt.edu

Presented at ASIST 2004 Annual Meeting; "Managing and Enhancing Information: Cultures and Conflicts" (ASIST AM 04), Providence, Rhode Island, November 13 - 18, 2004


Abstract

The analysis of contextual information in search engine query logs is an important, yet difficult task. Users submit few queries, and search multiple topics sometimes with closely related context. Identification of topic changes within a search session is an important branch of contextual information analysis. The purpose of this study is to propose a topic identification algorithm using artificial neural networks. A sample from the Excite data log is selected to train the neural network and then the neural network is used to identify topic changes in the data log. As a result, 100% of topic shifts were estimated correctly, with 77.8% precision in the overall dataset.


  
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