Abstract
The language model for information retrieval has statistical background and can adapt previous text information retrieval model. Therefore, this model has attracted much attention in recent years. This retrieval model considers only text information. However, we focus on the Web page retrieval in one of the retrieval tasks. Web pages also have some kind of features, so that we should consider another information for the Web page retrieval. Especially, Web pages consist the hyperlink information that is beneficial information for Web page retrieval. In this paper, we propose new retrieval approach considering a feature of term in neighboring Web pages using the hyperlink information.
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References
Charniak, E.: Statistical Language Learning. The MIT Press, Cambridge (1996)
Clarke, C.L.A., Craswell, N., Soboroff, I.: Overview of the trec 2009 web track. In: Text Retrieval Conference (TREC) (2009)
Hollander, M., Wolfe, D.A.: Nonparametric Statistical Methods. Wiley Interscience, Hoboken (1999)
Jelinek, F., Mercer, R.L.: Interpolated estimation of markov source parameters from sparse data. In: Proceeding of the Workshop on Pattern Recognition in Practice, pp. 381–397 (1980)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. In: SODA 1998: Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 668–677. Society for Industrial and Applied Mathematics, Philadelphia (1998)
Lawrence, P., Sergey, B., Rajeev, M., Terry, W.: The PageRank Citation Ranking: Bringing Order to the Web. Technical Report 1999-66, Stanford InfoLab (1999), http://ilpubs.stanford.edu:8090/422/
Liu, X., Croft, W.B.: Cluster-based retrieval using language models. In: SIGIR 2004: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 186–193. ACM, New York (2004), doi: http://doi.acm.org/10.1145/1008992.1009026
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 275–281. ACM, New York (1998), doi: http://doi.acm.org/10.1145/290941.291008
Song, F., Croft, W.: A general language model for information retrieval. In: CIKM 1999: Proceedings of the Eighth International Conference on Information and Knowledge Management, pp. 316–321. ACM, New York (1999), doi: http://doi.acm.org/10.1145/319950.320022
Tamura, K., Hatano, K., Yadohisa, H.: Characterizing web pages based on the query likelihoods of neighboring pages. In: Proceedings of the 5th International Conference on Digital Information Management (ICDIM 2010), pp. 392–397 (2010)
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Tamura, K., Hatano, K., Yadohisa, H. (2011). Calculating Query Likelihoods Based on Web Data Analysis. In: Watada, J., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22194-1_70
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DOI: https://doi.org/10.1007/978-3-642-22194-1_70
Publisher Name: Springer, Berlin, Heidelberg
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