Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 6th international conference on Intelligent user interfaces
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Subject categorization of query terms for exploring Web users' search interests
Journal of the American Society for Information Science and Technology
ACM SIGIR Forum
Query type classification for web document retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Categorizing web queries according to geographical locality
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Using the web to obtain frequencies for unseen bigrams
Computational Linguistics - Special issue on web as corpus
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Hourly analysis of a very large topically categorized web query log
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGKDD Explorations Newsletter
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Detecting dominant locations from search queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
KDD CUP-2005 report: facing a great challenge
ACM SIGKDD Explorations Newsletter
The Ferrety algorithm for the KDD Cup 2005 problem
ACM SIGKDD Explorations Newsletter
Classifying search engine queries using the web as background knowledge
ACM SIGKDD Explorations Newsletter
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Query enrichment for web-query classification
ACM Transactions on Information Systems (TOIS)
Automatic classification of Web queries using very large unlabeled query logs
ACM Transactions on Information Systems (TOIS)
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Using Google distance to weight approximate ontology matches
Proceedings of the 16th international conference on World Wide Web
Determining the user intent of web search engine queries
Proceedings of the 16th international conference on World Wide Web
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Improving search engines by query clustering
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
Modeling anchor text and classifying queries to enhance web document retrieval
Proceedings of the 17th international conference on World Wide Web
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Two novel feature selection approaches for web page classification
Expert Systems with Applications: An International Journal
Threshold selection for web-page classification with highly skewed class distribution
Proceedings of the 18th international conference on World wide web
Improving web page classification by label-propagation over click graphs
Proceedings of the 18th ACM conference on Information and knowledge management
A Web page classification system based on a genetic algorithm using tagged-terms as features
Expert Systems with Applications: An International Journal
Analyzing the effect of query class on document retrieval performance
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Collaborative pseudo-relevance feedback
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
When classifying search queries into a set of target categories, machine learning based conventional approaches usually make use of external sources of information to obtain additional features for search queries and training data for target categories. Unfortunately, these approaches rely on large amount of training data for high classification precision. Moreover, they are known to suffer from inability to adapt to different target categories which may be caused by the dynamic changes observed in both Web topic taxonomy and Web content. In this paper, we propose a feature-free classification approach using semantic distance. We analyze queries and categories themselves and utilizes the number of Web pages containing both a query and a category as a semantic distance to determine their similarity. The most attractive feature of our approach is that it only utilizes the Web page counts estimated by a search engine to provide the search query classification with respectable accuracy. In addition, it can be easily adaptive to the changes in the target categories, since machine learning based approaches require extensive updating process, e.g., re-labeling outdated training data, re-training classifiers, to name a few, which is time consuming and high-cost. We conduct experimental study on the effectiveness of our approach using a set of rank measures and show that our approach performs competitively to some popular state-of-the-art solutions which, however, frequently use external sources and are inherently insufficient in flexibility.