Web query classification

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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.

A Web query topic classification/categorization is a problem in information science. The task is to assign a Web search query to one or more predefined categories, based on its topics. The importance of query classification is underscored by many services provided by Web search. A direct application is to provide better search result pages for users with interests of different categories. For example, the users issuing a Web query "apple" might expect to see Web pages related to the fruit apple, or they may prefer to see products or news related to the computer company. Online advertisement services can rely on the query classification results to promote different products more accurately. Search result pages can be grouped according to the categories predicted by a query classification algorithm. However, the computation of query classification is non-trivial. Different from the document classification tasks, queries submitted by Web search users are usually short and ambiguous; also the meanings of the queries are evolving over time. Therefore, query topic classification is much more difficult than traditional document classification tasks.

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