Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Q2C@UST: our winning solution to query classification in KDDCUP 2005
ACM SIGKDD Explorations Newsletter
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learn from web search logs to organize search results
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Search advertising using web relevance feedback
Proceedings of the 17th ACM conference on Information and knowledge management
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
| Hi-index | 0.00 |
In web search, understanding the user intent plays an important role in improving search experience of the end users. Such an intent can be represented by the categories which the user query belongs to. In this work, we propose an information retrieval based approach to query categorization with an emphasis on learning category rankings. To carry out categorization we first represent a category by web documents (from Open Directory Project) that describe the semantics of the category. Then, we learn the category rankings for the queries using 'learning to rank' techniques. To show that the results obtained are consistent and do not vary across datasets, we evaluate our approach on two datasets including the publicly available KDD Cup dataset. We report an overall improvement of 20% on all evaluation metrics (precision, recall and F-measure) over two baselines: a text categorization baseline and an unsupervised IR baseline.