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 insufficiencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need different 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 difference 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 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.
- R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. ACM PRESS BOOKS, 1999. Google ScholarDigital Library
- P. Bailey, N. Craswell, and D. Hawking. Engineering a multi-purpose test collection for web retrieval experiments. Information Processing and Management, to appear. Google ScholarDigital Library
- S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1-7):107--117, 1998. Google ScholarDigital Library
- A. Broder. A taxonomy of web search. SIGIR Forum, 36(2), 2002. Google ScholarDigital Library
- W. B. Croft. Combining approaches to information retrieval. In Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval, pages 1--36. Kluwer Academic Publishers, 2000.Google Scholar
- CSIRO. Web research collections - trec web track. www.ted.cmis.csiro.au /TRECWeb/, 2001.Google Scholar
- E. Fox and J. Shaw. Combination of multiple searches. In Text REtrieval Conference (TREC-1), pages 243--252, 1993.Google Scholar
- D. Hawking and N. Craswell. Overview of the trec-2001 web track. In Text REtrieval Conference (TREC-10), pages 61--67, 2001.Google Scholar
- E. Jaynes. Information theory and statistical mechanics. Physics Review, 106(4):620--630, 1957.Google ScholarCross Ref
- J. H. Lee. Analyses of multiple evidence combination. In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 267--276, 1997. Google ScholarDigital Library
- C. D. Manning and H. Schutze. Foundations of Statistical Natural Language Processing. The MIT Press, 1999. Google ScholarDigital Library
- P. Ogilvie and J. Callan. Experiments using the lemur toolkit. In Text REtrieval Conference (TREC-10), pages 103--108, 2001.Google Scholar
- L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.Google Scholar
- J. M. Ponte. Language models for relevance feedback. In W. B. Croft, editor, Advances in Information Retrieval: Recent Research from the Center for Intelligent Information Retrieval, pages 73--95. Kluwer Academic Publishers, 2000.Google Scholar
- S. E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, and M. Gatford. Okapi at trec-3. In Text REtrieval Conference (TREC-2), pages 109--126, 1994.Google Scholar
- T. Westerveld, W. Kraaij, and D. Hiemstra. Retrieving web pages using content, links, urls and anchors. In Text REtrieval Conference (TREC-10), pages 663--672, 2001.Google Scholar
- K. Yang. Combining text and link-based retrieval methods for web ir. In Text REtrieval Conference (TREC-10), pages 609--618, 2001.Google Scholar
Index Terms
- Query type classification for web document retrieval
Recommendations
Context-aware query classification
SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrievalUnderstanding users'search intent expressed through their search queries is crucial to Web search and online advertisement. Web query classification (QC) has been widely studied for this purpose. Most previous QC algorithms classify individual queries ...
Modeling anchor text and classifying queries to enhance web document retrieval
WWW '08: Proceedings of the 17th international conference on World Wide WebSeveral types of queries are widely used on the World Wide Web and the expected retrieval method can vary depending on the query type. We propose a method for classifying queries into informational and navigational types. Because terms in navigational ...
Varying approaches to topical web query classification
SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrievalTopical classification of web queries has drawn recent interest because of the promise it offers in improving retrieval effectiveness and efficiency. However, much of this promise depends on whether classification is performed before or after the query ...
Comments