Name
Abstract | ||
|---|---|---|
Classifying web queries into predefined target categories, also known as web query classification, is important to improve search relevance and online advertising. Web queries are however typically short, ambiguous and in constant flux. Moreover, target categories often lack standard taxonomies and precise semantic descriptions. These challenges make the web query classification task a non-trivial problem. In this paper, we present two complementary approaches for the web query classification task. First is the enrichment method that uses the World Wide Web (WWW) to enrich target categories and further models the web query classification as a search problem. Our second approach, the reductionist approach, works by reducing web queries to few central tokens. We evaluate the two approaches based on few thousands human labeled local and non-local web queries. From our study, we find the two approaches to be complementary to each other as the reductionist approach exhibits high precision but low recall, whereas the enrichment method exhibits high recall but low precision. |
Year | Venue | Keywords |
|---|---|---|
2011 | Lecture Notes in Computer Science | Query Classification,Unsupervised,Semi-supervised,Bayesian Approach |
DocType | Volume | ISSN |
Conference | 7064 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ritesh Agrawal | 1 | 12 | 2.74 |
| Xiaofeng Yu | 2 | 67 | 6.22 |
| Irwin King | 3 | 6751 | 325.94 |
| Remi Zajac | 4 | 46 | 4.50 |