Abstract
The fast vector space and probabilistic methods use the term counts and the slower proximity methods use term positions. We present the spectral-based information retrieval method which is able to use both term count and position information to obtain high precision document rankings. We are able to perform this, in a time comparable to the vector space method, by examining the query term spectra rather than query term positions. This method is a generalisation of the vector space method (VSM). Therefore, our spectral method can use the weighting schemes and enhancements used in the VSM.
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References
Zobel, J., Moffat, A.: Exploring the similarity space. ACM SIGIR Forum 32, 18–34 (1998)
Jones, K.S., Walker, S., Robertson, S.E.: A probabilistic model of information retrieval: development and comparative experiments. Information Processing and Management 36, 779–808 (2000)
Clarke, C.L.A., Cormack, G.V.: Shortest-substring retrieval and ranking. ACM Transactions on Information Systems 18, 44–78 (2000)
Hawking, D., Thistlewaite, P.: Relevance weighting using distance between term occurrences. Technical Report TR-CS-96-08, The Australian National University (1996)
Harman, D.: Relevance feedback revisited. In: Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 1–10. ACM Press, New York (1992)
Jing, Y., Croft, W.B.: An association thesaurus for information retrieval. In: Proc. of Intelligent Multimedia Retrieval Systems and Management Conference (RIAO), pp. 146–160 (1994)
Park, L.A.F., Palaniswami, M., Kotagiri, R.: Internet document filtering using fourier domain scoring. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 362–373. Springer, Heidelberg (2001)
Park, L.A.F., Ramamohanarao, K., Palaniswami, M.: Fourier domain scoring: A novel document ranking method. IEEE Transactions on Knowledge and Data Engineering 16, 529–539 (2004)
Park, L.A.F., Palaniswami, M., Ramamohanarao, K.: A novel web text mining method using the discrete cosine transform. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 385–396. Springer, Heidelberg (2002)
Park, L.A.F., Ramamohanarao, K., Palaniswami, M.: A new implementation technique for fast spectral based document retrieval systems. In: Kumar, V., Tsumoto, S. (eds.) 2002 IEEE International Conference on Data Mining, pp. 346–353. IEEE Computer Society, Los Alamitos (2002)
Park, L.A.F., Palaniswami, M., Ramamohanarao, K.: A novel document ranking method using the discrete cosine transform. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
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Ramamohanarao, K., Park, L.A.F. (2004). Spectral-Based Document Retrieval. In: Maher, M.J. (eds) Advances in Computer Science - ASIAN 2004. Higher-Level Decision Making. ASIAN 2004. Lecture Notes in Computer Science, vol 3321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30502-6_30
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DOI: https://doi.org/10.1007/978-3-540-30502-6_30
Publisher Name: Springer, Berlin, Heidelberg
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