Foundations of statistical natural language processing
Foundations of statistical natural language processing
Searching the Web: the public and their queries
Journal of the American Society for Information Science and Technology
Information Retrieval
A critical examination of TDT's cost function
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Subject categorization of query terms for exploring Web users' search interests
Journal of the American Society for Information Science and Technology
Automatic Subject Categorization of Query Terms for Filtering Sensitive Queries in Multimedia Search
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
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
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Detecting dominant locations from search queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting online commercial intention (OCI)
Proceedings of the 15th international conference on World Wide Web
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)
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
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Learning search tasks in queries and web pages via graph regularization
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Query classification based on index association rule expansion
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Towards the taxonomy-oriented categorization of yellow pages queries
ACM Transactions on Internet Technology (TOIT)
Answering math queries with search engines
Proceedings of the 21st international conference companion on World Wide Web
Confidence-aware graph regularization with heterogeneous pairwise features
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Cost-sensitive learning for large-scale hierarchical classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Analyzing, Detecting, and Exploiting Sentiment in Web Queries
ACM Transactions on the Web (TWEB)
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Web query classification is an effective way to understand Web user intents, which can further improve Web search and online advertising relevance. However, Web queries are usually very short which cannot fully reflect their meanings. What is more, it is quite hard to obtain enough training data for training accurate classifiers. Therefore, previous work on query classification has focused on two issues. One is how to represent Web queries through query expansion. The other is how to increase the amount of training data. In this paper, we took product query classification as an example, which is to classify Web queries into a predefined product taxonomy, and systematically studied the impact of query expansion and the size of training data. We proposed two methods of enriching Web queries and three approaches of collecting training data. Thereafter, we conducted a series of experiments to compare the classification performance of using different combinations of training data and query representations over a real data set. The data set consists of hundreds of thousands queries collected from a popular commercial search engine. From the experiments, we found some interesting observations, which were not discussed before. Finally, we proposed an effective and efficient product query classification method based on our observations.