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Varying approaches to topical web query classification (2007)
| Venue: | Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval |
| Citations: | 20 - 0 self |
BibTeX
@INPROCEEDINGS{Beitzel07varyingapproaches,
author = {Steven M. Beitzel and Eric C. Jensen and Abdur Chowdhury and Ophir Frieder},
title = {Varying approaches to topical web query classification},
booktitle = {Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval},
year = {2007},
pages = {783--784}
}
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Abstract
Topical classification of web queries has drawn recent interest because of the promise it offers in improving retrieval effectiveness and efficiency. However, much of this promise depends on whether classification is performed before or after the query is used to retrieve documents. We examine two previously unaddressed issues in query classification: pre vs. post-retrieval classification effectiveness and the effect of training explicitly from classified queries vs. bridging a classifier trained using a document taxonomy. Bridging classifiers map the categories of a document taxonomy onto those of a query classification problem to provide sufficient training data. We find that training classifiers explicitly from manually classified queries outperforms the bridged classifier by 48 % in F1 score. Also, a pre-retrieval classifier using only the query terms performs merely 11 % worse than the bridged classifier which requires snippets from retrieved documents.
Keyphrases
topical web query classification document taxonomy unaddressed issue query classification classified query pre-retrieval classifier query term post-retrieval classification effectiveness query classification problem topical classification recent interest web query f1 score pre v sufficient training data retrieval effectiveness
