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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\u2010based performance measures, whereas logs with more topic continuations tend to provide better results on continuation\u2010based performance measures.<\/jats:p>","DOI":"10.1002\/meet.1450430129","type":"journal-article","created":{"date-parts":[[2007,10,10]],"date-time":"2007-10-10T13:52:00Z","timestamp":1192024320000},"page":"1-27","source":"Crossref","is-referenced-by-count":2,"title":["Investigating the performance of automatic new topic identification across multiple datasets"],"prefix":"10.1002","volume":"43","author":[{"given":"H. 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