Investigating the performance of automatic new topic identification across multiple datasets 1

Ozmutlu, H. C., Cavdur, F., Spink, Amanda H., & Ozmutlu, S. (2006) Investigating the performance of automatic new topic identification across multiple datasets 1. In Proceedings of the American Society for Information Science and Technology, Wiley & Blackwell Publishing.

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Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine. In this study, we investigate whether the application of neural networks for automatic new topic identification are more successful on some search engines than others. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that query logs with more topic shifts tend to provide more successful results on shift-based performance measures, whereas logs with more topic continuations tend to provide better results on continuation-based performance measures.

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ID Code: 47859
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1002/meet.1450430129
ISSN: 1550-8390
Divisions: Current > Research Centres > Office of Education Research
Current > QUT Faculties and Divisions > Faculty of Education
Copyright Owner: Wiley Blackwell
Deposited On: 20 Dec 2011 06:49
Last Modified: 18 Oct 2016 00:40

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