Geodesic
Latent semantic indexing for patent documents
International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 4, pp. 551-560.

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Since the huge database of patent documents is continuously increasing, the issue of classifying, updating and retrieving patent documents turned into an acute necessity. Therefore, we investigate the efficiency of applying Latent Semantic Indexing, an automatic indexing method of information retrieval, to some classes of patent documents from the United States Patent Classification System. We present some experiments that provide the optimal number of dimensions for the Latent Semantic Space and we compare the performance of Latent Semantic Indexing (LSI) to the Vector Space Model (VSM) technique applied to real life text documents, namely, patent documents. However, we do not strongly recommend the LSI as an improved alternative method to the VSM, since the results are not significantly better.
Keywords: Latent Semantic Indexing (LSI), singular value decomposition (SVD), vector space model (VSM), patent classification
Mots-clés : indeksowanie semantyczne, rozkład wartości szczególnych, model przestrzeni wektorowej, klasyfikacja patentowa 
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Moldovan, A.; Boţ, R. I.; Wanka, G. Latent semantic indexing for patent documents. International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 4, pp. 551-560. http://geodesic.mathdoc.fr/item/IJAMCS_2005_15_4_a11/

[1] ARPACK (2005): Arnoldi package. — Available at: http://www.ime.unicamp.br/chico/arpack++.

[2] Bartell B.T., Cotrell G.W. and Belew R.K. (1992): Latent semantic indexing is an optimal special case of multidimensional scaling. — Proc. ACM/SIGIR’92 Conf., Copenhagen, Denmark, pp.161–167.

[3] Berry M.W., Dumais S.T. and O’Brien G.W. (1995): Using linear algebra for intelligent information retrieval. — SIAM Rev., Vol. 37, No. 4, pp. 573–595.

[4] Deerwester S., Dumais S.T., Furnas G.W., Landauer T.K. and Harshman R. (1990): Indexing by latent semantic analysis. —J. Amer. Soc. Inf. Sci., Vol. 41, No. 6, pp. 391–407.

[5] Ding C.H.Q. (1999): A similarity-based probability model for latent semantic indexing. — Proc. 22nd ACM/SIGIR Conf., Berkley, CA, pp. 58–65.

[6] Dumais S.T. (1991): Improving the retrieval of information from external sources. — Behav. Res. Meth. Instrum. Comput., Vol. 23, No. 2, pp. 229–236.

[7] Dumais S.T. (1995): Using LSI for information filtering: TREC-3 experiments. — Proc. 3rd Text REtrieval Conf., TREC3, Gaithersburg, MD, pp. 219–230.

[8] Fuhr N. (1989): Models for retrieval with probabilistic indexing. — Inf. Process. Manag., Vol. 25, No. 1, pp. 55–72.

[9] Fuhr N. (1992): Probabilistic models in information retrieval. — Comput. J., Vol. 35, No. 3, pp. 243–255.

[10] Hull D. (1994): Improving text retrieval for the routing problem using latent semantic indexing. —Proc. 17th ACM/SIGIR Conf., Dublin, Ireland, pp. 282–290.

[11] Hull D. (1996): Stemming algorithms: A case study for detailed evaluation. — J. Amer. Soc. Inform. Sci., Vol. 47, No. 1, pp. 70–84.

[12] Jessup E.R. and Martin J.H. (2001): Taking a new look at the latent semantic analysis approach to information retrieval. — Proc. SIAM Workshop Computational Information Retrieval, Raleigh, NC, pp. 121–144.

[13] Kolda T.G. and O’Leary D.P. (1998): A semidiscrete matrix decomposition for latent semantic indexing information retrieval. — ACM Trans. Inf. Syst. (TOIS), Vol. 16, No. 4, pp. 322–346.

[14] Landauer T.K., Foltz P. and Laham D. (1998): Introduction to latent semantic analysis. — Discourse Processes, Vol. 25, pp. 259–284.

[15] MED (2005): Medlon collection. —Available at: ftp://ftp.cs.cornell.edu/pub/smart/med.

[16] Papadimitriou C.H., Raghavan P., Tamaki H. and Vempala S. (1998): Latent semantic indexing: A probabilistic analysis. — Proc. Symp. Principles of Database Systems, PODS, Seattle, Washington, pp. 150–168.

[17] PorterStemmer (2005): The Porter stemming algorithm. — Available at: http://www.tartarus.org/martin/PorterStemmer.

[18] Salton G. (1971): The SMART Retrieval System: Experiments in Automatic Document Processing. — Englewood Cliffs, NJ: Prentice Hall.

[19] Schütze H. (1992): Dimensions of meaning. — Proc. Conf. Supercomputing ’92, Minneapolis, MN, pp. 787–796.

[20] Schütze, H. (1998): Automatic word sense discrimination. — Comput. Linguist., Vol. 24, No. 1, pp. 97–124.

[21] SMART (2005): SMART’s English stoplist. — Available at: ftp://ftp.cs.cornell.edu/pub/smart/english.stop.

[22] TIME (2005): Time magazine collection. —Available at: ftp://ftp.cs.cornell.edu/pub/smart/time.

[23] Story R.E. (1996): An explanation of the effectiveness of Latent Semantic Indexing by means of a Bayesian regression model. — Inf. Process. Manag., Vol. 32, No. 3, pp. 329–344.

[24] UPSTO (2005): United States Patent and Trademark Office. — Available at: http://www.uspto.gov.

[25] Zha H., Marques O. and Simon H. (1998): A subspace-based model for information retrieval with applications in latent semantic indexing. — Proc. Conf. Irregular ’98, Barkeley, CA, pp. 29–42.