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2005 | 15 | 4 | 551-560
Tytuł artykułu

Latent Semantic Indexing for patent documents

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
15
Numer
4
Strony
551-560
Opis fizyczny
Daty
wydano
2005
otrzymano
2005-04-12
poprawiono
2005-09-11
Twórcy
  • Faculty of Mathematics, Chemnitz University of Technology, D-09107 Chemnitz, Germany
  • Faculty of Mathematics, Chemnitz University of Technology, D-09107 Chemnitz, Germany
autor
  • Faculty of Mathematics, Chemnitz University of Technology, D-09107 Chemnitz, Germany
Bibliografia
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  • Bartell B.T., Cotrell G.W. and Belew R.K. (1992): Latent semantic indexing is an optimal special case ofmultidimensional scaling. - Proc. ACMSIGIR'92 Conf., Copenhagen, Denmark, pp.161-167.
  • 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.
  • 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.
  • Ding C.H.Q. (1999): A similarity-based probability model for latent semantic indexing. - Proc. 22nd ACM/SIGIR Conf., Berkley, CA, pp. 58-65.
  • Dumais S.T. (1991): Improving the retrieval of information from external sources. - Behav. Res. Meth. Instrum. Comput., Vol. 23, No. 2, pp. 229-236.
  • Dumais S.T. (1995): Using LSI for information filtering: TREC-3 experiments. - Proc. 3rd Text REtrieval Conf., TREC3, Gaithersburg, MD, pp. 219-230.
  • Fuhr N. (1989): Models for retrieval with probabilistic indexing. - Inf. Process. Manag., Vol. 25, No. 1, pp. 55-72.
  • Fuhr N. (1992): Probabilistic models in information retrieval. - Comput. J., Vol. 35, No. 3, pp. 243-255.
  • Hull D. (1994): Improving text retrieval forthe routing problem using latent semantic indexing. - Proc. 17th ACM/SIGIR Conf., Dublin, Ireland, pp. 282-290.
  • Hull D. (1996): Stemming algorithms: A case study for detailed evaluation. - J. Amer. Soc. Inform. Sci., Vol. 47, No. 1, pp. 70-84.
  • 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.
  • 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.
  • Landauer T.K., Foltz P. and Laham D. (1998): Introduction to latent semantic analysis. - Discourse Processes, Vol. 25, pp. 259-284.
  • MED (2005): Medlon collection. - Available at: ftp://ftp.cs.cornell.edu/pub/smart/med
  • 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.
  • PorterStemmer (2005): The Porter stemming algorithm. - Available at: http://www.tartarus.org /~martin/PorterStemmer
  • Salton G. (1971): The SMART Retrieval System: Experiments in Automatic Document Processing. - Englewood Cliffs, NJ: Prentice Hall.
  • Schutze H. (1992): Dimensions of meaning. - Proc. Conf. Supercomputing '92, Minneapolis, MN, pp. 787-796.
  • Schutze, H. (1998): Automatic word sense discrimination. - Comput. Linguist., Vol. 24, No. 1, pp. 97-124.
  • SMART (2005): SMART's English stoplist. - Available at: ftp://ftp.cs.cornell.edu/pub/smart/english.stop
  • TIME (2005): Time magazine collection. - Available at: ftp://ftp.cs.cornell.edu/pub/smart/time
  • 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.
  • UPSTO (2005): United States Patent and Trademark Office. - Available at: http://www.uspto.gov
  • 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.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv15i4p551bwm
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