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Integration of multiple evidences based on a query type for web search [An article from: Information Processing and Management]
This digital document is a journal article from Information Processing and Management, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The massive and heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web pages are not enough to find answer pages. PageRank compensates for the insufficiencies of content information. The content information and PageRank are combined to get better results. However, static combination of multiple evidences may lower the retrieval performance. We have to use different strategies to meet the need of a user. We can classify user queries as three categories according to users' intent, the topic relevance task, the homepage finding task, and the service finding task. In this paper, we present a user query classification method. The difference of distribution, mutual information, the usage rate as anchor texts and the POS information are used for the classification. After we classified a user query, we apply different algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.
Item tags:
information processing, multiple evidences, article information processing, content, task, user, information, article information |
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