Web query classification

Importance and Challenges of Web Query Classification
– Web query classification is a problem in information science that assigns a Web search query to predefined categories based on its topics.
– Query classification improves search result pages for users with different interests and helps online advertisement services promote products accurately.
– Query classification is more difficult than traditional document classification tasks.
– Web query classification faces difficulties such as short and noisy queries, multiple meanings of queries, and the evolution of query and category meanings over time.
– Manually labeled training data for query classification is expensive.

Methods to Overcome Difficulties in Web Query Classification
– Query-enrichment based methods use search engines to enrich user queries with top-ranked result page snippets.
– Intermediate taxonomy based methods build a bridging classifier on an intermediate taxonomy, such as Open Directory Project.
– Query clustering methods associate related queries by clustering session data.
– Selectional preference-based methods exploit association rules between query terms.
– Unlabeled query logs can be used as a source of unlabeled data to aid in automatic query classification.

Applications of Web Query Classification
– Metasearch engines blend top results from multiple search engines based on query categories.
– Vertical search focuses on specific domains and addresses niche information needs.
– Online advertising provides relevant advertisements to Web users based on their interests.
– Web query classification is essential for services like metasearch engines, vertical search, and online advertising.
– Understanding Web users’ search intents through their queries is crucial for these services.

Related Concepts and References
– Document classification, web search query, information retrieval, query expansion, Naive Bayes classifier, support vector machines, meta search, vertical search, and online advertising are related concepts.
– References include the KDDCUP 2005 dataset and various papers on query classification and web query understanding.

Further Reading
– Further reading includes topics such as learning-based web query understanding, a PhD thesis on web query understanding, the Z39.50 protocol, and its use in web query understanding. This group provides more in-depth information on specific aspects of web query classification.