Web query classification: Difference between revisions

Revision as of 16:58, 13 May 2009

Revision as of 23:39, 30 May 2009

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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:

=== How to derive an appropriate feature representation for Web queries? ===

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 setup a [[vector space model]] for classification is not appropriate.

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give the answers:

=== How to adapt the changes of the queries and categories over time? ===

The meanings of queries may also evolve over time. Therefore, the old labeled training queries may be out-of-data and useless soon. How to make the classifier adaptive over time becomes a big issue. For example, the word "''Barcelona''" has a new meaning of the new micro-processor of AMD, while it refers to a city or football club before 2007. The distribution of the meanings of this term is therefore a function of time on the Web.

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== Applications ==

* '''[[metasearch|Metasearch engines]]''' send a user's query to multiple search engines and blend the top results from each into one overall list. The search engine can organize the large number of Web pages in the search results, according to the potential categories of the issued query, for the convenience of Web users' navigation.<br />

* '''[[Vertical search]]''', compared to general search, focuses on specific domains and addresses the particular information needs of niche audiences and professions. Once the search engine can predict the category of information a Web user is looking for, it can select a certain vertical search engine automatically, without forcing the user to access the vertical search engine explicitly.

* '''[[Online advertising]]'''<ref>[http://www.kdd2007.com/workshops.html#adkdd Data Mining and Audience Intelligence for Advertising (ADKDD'07)], KDD workshop 2007</ref><ref>[http://research.yahoo.com/workshops/troa-2008/ Targeting and Ranking for Online Advertising (TROA'08)], WWW workshop 2008</ref> aims at providing interesting advertisements to Web users during their search activities. The search engine can provide relevant advertising to Web users according to their interests, so that the Web users can save time and effort in research while the advertisers can reduce their advertising costs.