Paper Info
Title
Classifying search queries using the Web as a source of knowledge
Abstract
We propose a methodology for building a robust query classification system that can identify thousands of query classes, while dealing in real time with the query volume of a commercial Web search engine. We use a pseudo relevance feedback technique: given a query, we determine its topic by classifying the Web search results retrieved by the query. Motivated by the needs of search advertising, we primarily focus on rare queries, which are the hardest from the point of view of machine learning, yet in aggregate account for a considerable fraction of search engine traffic. Empirical evaluation confirms that our methodology yields a considerably higher classification accuracy than previously reported. We believe that the proposed methodology will lead to better matching of online ads to rare queries and overall to a better user experience.
Year
DOI
Venue
2009
10.1145/1513876.1513877
TWEB
Keywords
Field
DocType
query classiflcation,commercial web search engine,query classification,methodology yield,rare query,search engine traffic,query volume,search advertising,proposed methodology,web search result,additional key words and phrases: pseudo relevance feedback,web search,query class,classifying search,pseudo relevance feedback,robust query classification system,user experience,search engine,machine learning,web search engine,real time
Web search engine,Data mining,Query language,Computer science,Web query classification,Search-oriented architecture,Artificial intelligence,Query optimization,Web search query,World Wide Web,Query expansion,Information retrieval,Sargable,Machine learning
Journal
Volume
Issue
ISSN
3
2
1559-1131
Citations 
PageRank 
References 
25
1.06
33
Authors
7
Name
Order
Citations
PageRank
Evgeniy Gabrilovich14573224.48
Andrei Broder27357920.20
Marcus Fontoura3111661.74
Amruta Joshi41878.67
Vanja Josifovski52265148.84
Lance Riedel645419.42
Zhang, Tong77126611.43