Query Type Classification for Web Document Retrieval (2003)
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author = {In-Ho Kang Department and In-ho Kang},
title = {Query Type Classification for Web Document Retrieval},
booktitle = {In Proceedings of the 26th annual international ACM SIGIR conference on Research and Development in Information Retrieval},
year = {2003},
pages = {64--71},
publisher = {ACM Press}
}
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Abstract:
The heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web documents are not enough to find good answer documents. Link information and URL information compensates for the insu #ciencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need di#erent strategies to find target documents according to a query type. We can classify user queries as three categories, the topic relevance task, the homepage finding task, and the service finding task. In this paper, a user query classification scheme is proposed. This scheme uses the di#erence of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification. After we classified a user query, we apply di#erent 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.
