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→How to derive an appropriate feature representation for Web queries?: === Derive an appropriate feature representation for Web queries === |
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Web query topic classification is to automatically assign a query to some predefined categories. Different from the traditional document classification tasks, there are several major difficulties which hinder the progress of Web [[query understanding]]:
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Many queries are short and query terms are noisy. As an example, in the KDDCUP 2005 dataset, queries containing 3 words are most frequent (22%). Furthermore, 79% queries have no more than 4 words. A user query often has multiple meanings. For example, "''apple''" can mean a kind of fruit or a computer company. "''Java''" can mean a programming language or an island in Indonesia. In the KDDCUP 2005 dataset, most of the queries contain more than one meaning. Therefore, only using the keywords of the query to set up a [[vector space model]] for classification is not appropriate.
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