Improving automatic query classification via semi-supervised learning

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Improving automatic query classification via semi-supervised learning (2005)

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by Steven M. Beitzel , Eric C. Jensen , Ophir Frieder

Venue:	In The Fifth IEEE International Conference on Data Mining
Citations:	44 - 7 self

BibTeX

@INPROCEEDINGS{Beitzel05improvingautomatic,
    author = {Steven M. Beitzel and Eric C. Jensen and Ophir Frieder},
    title = {Improving automatic query classification via semi-supervised learning},
    booktitle = {In The Fifth IEEE International Conference on Data Mining},
    year = {2005},
    pages = {42--49},
    publisher = {IEEE Computer Society}
}

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Abstract

Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose web search systems. Such classification becomes critical if the system is to return results not just from a general web collection but from topic-specific back-end databases as well. Maintaining sufficient classification recall is very difficult as web queries are typically short, yielding few features per query. This feature sparseness coupled with the high query volumes typical for a large-scale search service makes manual and supervised learning approaches alone insufficient. We use an application of computational linguistics to develop an approach for mining the vast amount of unlabeled data in web query logs to improve automatic topical web query classification. We show that our approach in combination with manual matching and supervised learning allows us to classify a substantially larger proportion of queries than any single technique. We examine the performance of each approach on a real web query stream and show that our combined method accurately classifies 46 % of queries, outperforming the recall of best single approach by nearly 20%, with a 7 % improvement in overall effectiveness. 1.

Keyphrases

semi-supervised learning automatic query classification manual matching sufficient classification recall user query web query large-scale search service increased effectiveness automatic topical web query classification unlabeled data feature sparseness single approach real web query stream topic-specific back-end database general-purpose web search system single technique computational linguistics vast amount general web collection high query combined method overall effectiveness accurate topical classification web query log

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