Improving Automatic Query Classification via Semi-Supervised Learning

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...

• Type: Conference
• Year: 2005
• Where: ICDM
• Authors: Steven M. Beitzel, Eric C. Jensen, Ophir Frieder, David D. Lewis, Abdur Chowdhury, Aleksander Kolcz
• Keywords: Data Mining, ICDM 2005, Supervised Learning, Web Query, Web Query Classification