skip to main content
article
Free Access

Exploration of text collections with hierarchical feature maps

Authors Info & Claims
Published:01 July 1997Publication History
First page image

References

  1. 1 T. Bayer, I. Renz, M. Stein, and U. Kressel. Domain and language independent feature extraction for statistical text categorization. In Proc o} the Workshop on Language Engineering for Document Analyais and Recognition, Sussex, United Kingdom, 1996.Google ScholarGoogle Scholar
  2. 2 R. Belew. A connectionist approach to conceptual information retrieval. In Proc oy the {nt'l Conference on A r. tificiai Intelligence and Law (ICAIL'87J, Breton, MA, 1987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3 C. M. Bishop, M. Svens#n, and C. K. 1. WiBiams. GTM: A a principled alternative to the self-organizing map. In Proc of the lnt'l Cony on Artificial Neural Network8 ({CANN'96), Bochum, Germany, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 4 C. M. Bishop, M. Svens#n, and C. K. 1. Williams. GTM: The generative topographic mapping. Technical Report NCRG/96/015, Aston University, Neural Computing Research Group, http://www.ncrg.aston.ac.uk, Birmingham, United Kingdom, 1996.Google ScholarGoogle Scholar
  5. 5 G. A. Carpenter and S. Grossberg. The ART of adaptive pattern recognition by a self-organizing neural network. IEEE Computer, 21(3), 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. 6 M. Cottrell and E. de Bodt. A Kohonen map representation to avoid misleading interpretations, in Proc of the European Symposium on Artificial Neural Networks (ESANN'96), Brugge, Belgium, 1996.Google ScholarGoogle Scholar
  7. 7 M. Cottrell and J.-C. Fort. Etude d'un proees. sus d'auto-organisation. Annales de l'lnstitut Henri Poincard, 23(1), 1987.Google ScholarGoogle Scholar
  8. 8 M. Cottrell, J.-C. Fort, and G. Pages. Two or three things that we know about the Kohonen algorithm. In Proc of the European Symposium on Artificial Neural Networks (ESANN'94), Bruxelles, Belgium, 1994.Google ScholarGoogle Scholar
  9. 9 F. Crestiani. Learning strategies for an adaptive information retrieval system using neural networks, in Proc o! the IEEE lnt'! Con/on Neural Networks (ICNN'93)# San Francisco, California, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 10 C. J. Crouch, D. B. Crouch, and K. Nareddy. A conneetionist model for information retrieval based on the vector space model, lnt'l Journal o{ Expert Systems, 7(2), 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 11 S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Hashman. Indexing by latent semantic analysis. Journal of the American Society }or Information Science, 41(6), 1990.Google ScholarGoogle Scholar
  12. 12 B. Fritzke. Growing Cell Structures: A self-organizing network for unsupervised and supervised learning. Neural Networks, 7(9), 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. 13 K. E. Gorlen. NIH class library reference manual. National Institutes of Health, Bethesda, Maryland, 1990.Google ScholarGoogle Scholar
  14. 14 K. E. Gor}en, S. Orlow, and P. Plexieo. Abstraction and Object-Oriented Programming in C-i--l-. John Wiley, New York, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15 T. Honkela, S. Kaski, K. Lagus, and T. Kohonen. Newsgroup exploration with WEBSOM method and browsing interface. Technical Report A32, Helsinki University of Technology, Laboratory of Computer and Information Science, http://websom.hut.fi, Espoo, Finland, 1996.Google ScholarGoogle Scholar
  16. 16 T. Honkela, V. Pulkki, and T. Kohonen. Contextual relations of words in Grimm tales analyzed by selforganizing maps. In Proc o} the lnt'l Con} on Artificial Neural Networks (ICA NN'95), Paris, France, 1995.Google ScholarGoogle Scholar
  17. 17 A. K. Jain and R. D. Dubes. Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. 18 !. T. Jolliffe. Principal Component Analysis. Springer- Verlag, Berlin, 1986.Google ScholarGoogle ScholarCross RefCross Ref
  19. 19 E. R. Kandel, S. A. Siegelbaum, and J. H. Schwartz. Synaptie transmission. In E. R. Kandel, J. H. Schwartz, and T. M. Jessell, editors, Principles o} Neural Science. Elsevier, New York, 1991.Google ScholarGoogle Scholar
  20. 20 S. Keane, V. Ratnaike, and R. Wilkinson. Hierarchical news filtering. In Proc of the Int'l Con} on Practical Aspects of Knowledge Management, Basel, Switzerland, 1996.Google ScholarGoogle Scholar
  21. 21 M. K#ihle and D. Merkl. Visualizing similarities in high dimensional input spaces with a growing and splitting neural network. In Proc of the {nt'l Conf on Artificial Neural Networks ({CANN'96), Bochum, Germany, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22 T. Kohonen. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43, 1989.Google ScholarGoogle Scholar
  23. 23 T. Kohonen. Generalizations of the serf-organizing map. In Proc of the lnt'! Joint Conf on Neural Networks (IJCNN'93), Nagoya, Japan, 1993.Google ScholarGoogle Scholar
  24. 24 T. Kohonen. Sell-organizing maps. Springer-Verlag, Berlin, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. 25 T. Kohonen, J. Hynninen, J. Kangas, and J. Lanksonen. SOM-PAK: The self-organizing map program package, Version 3.1. Helsinki University of Technology, Laboratory of Computer and Information Science, http://nucleus.hut.ti, Espoo, Finland, 1995.Google ScholarGoogle Scholar
  26. 26 T. Kohonen, S. Kaski, K. Lagus, and T. Honkela. Very large two-level SOM for the browsing of newsgroups. In Proc of the {at 7 Conf on Artificial Neural Networks (ICA NN'96), Bochum, Germany, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. 27 M. A. Kraaijveld, J. Mao, and A. K. Jain. A nonlinear projection method based on Kohonen's topology preserving maps. In Proc of the lnt'l Conference on Pattern Recognition (1CPR '92), 1992.Google ScholarGoogle Scholar
  28. 28 K. Lagus, T. Honkela, S. Kaski, and T. Kohonen. Selforga#zing maps of document collections: A new approach to interactive exploration. In Proc o.f the lnt'! Conf on Knowledge Discovery and Data Mining (KDD- 96), Portland, OR, 1996.Google ScholarGoogle Scholar
  29. 29 X. Lin, D. Soergel, and G. Marchionini. A selforganizing semantic map for information retrieval. In Proc of the A CM SIGIR lnt'l Cony on Research and Development in Information Retrieval (SIG{R'91), Chicago, IL, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. 30 D. Merkl. SelJ-Organization o} Software Libraries: An Artificial Neural Network Approach. Phi) thesis, institut fiir Angewandte lnformatik mad Informationssysteme, Universit/it Wien, 1994.Google ScholarGoogle Scholar
  31. 31 D. Merld. A eonnectionist view on document classification. In Proc o.f the Australasian Database Con/ (ADC'95), Adelaide, SA, 1995.Google ScholarGoogle Scholar
  32. 32 D. Merkl. Content-based document classification with highly compressed input data. In Proc o/the lnt'l Conf on Artificial Neural Networks (ICA NN'95), Pads, France, 1995.Google ScholarGoogle Scholar
  33. 33 D. Merld. Content-based software classification by selforganization. In Proc of the IEEE lnt'l Cony on Neural Networks ({CNN'95), Perth, WA, 1995.Google ScholarGoogle Scholar
  34. 34 D. Merld. The effect of lateral inhibition on learning speed and precision of a self-organizing map. In Proe o} the Australian Con/on Neural Networks, Sydney, NSW, 1995.Google ScholarGoogle Scholar
  35. 35 D. Merld. Exploration of document collections with self-organizing maps: A novel approach to similarity representation. In Proc of the European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'97}, Trondheim, Norway, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. 36 D. Merkl and A. Rauber. Alternative ways for cluster visualization in self-organizing maps. In Proc of the Workshop on Self.Organizing Maps, Espoo, Finland, 1997.Google ScholarGoogle Scholar
  37. 37 D. Merkl and A. Rauber. On the similarity of eagles, hawks, and cows: Visualization of similarity in selforganizing maps. In Proc of the lnt'l Workshop Fuzzy- Neuro-Systems'97, Soest, Germany, 1997.Google ScholarGoogle Scholar
  38. 38 D. Merkl, E. Sehweighofer, and W. Winiwarter. CON- CAT: Connotation analysis of thesauri based on the interpretation of context meaning. In Proc of the lnt'l Conference on Database and Expert Systems A pplico. tions (DEXA '9#), Athens, Greece, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. 39 E. Merlo, I. McAdam, and R. De Mori. Source code informal information analysis using conneetionist models. In Proc of the lnt'l Joint Conference on Artificial Intelligence (IjCAI'93), Chamb#ry, France, 1993.Google ScholarGoogle Scholar
  40. 40 R. Miikkulainen. Script recognition with hierarchical feature maps. Connection Science, 2, 1990.Google ScholarGoogle Scholar
  41. 41 R. Miikkulainen. Self-organizing process based on lateral inhibition and synaptic resource redistribution. In Proc of the lnt'l Conf on Artificial Neural Networks (ICANN'91), Espoo, Finland, 1991.Google ScholarGoogle Scholar
  42. 42 R. Miikkulainen. "iYaee feature map: A model of episodic associative memory. Biological Cybernetics, 66, 1992.Google ScholarGoogle Scholar
  43. 43 R. Miikkulainen. Subsymbolic Natural Language Processing: An integrated model of scripts, lexicon, and memory. MIT-Press, Cambridge, MA, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. 44 B. D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, United Kingdom, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. 45 H. Ritter and T. Kohonen. Self-organizing semantic maps. Biological Cybernetics, 61, 1989.Google ScholarGoogle Scholar
  46. 46 D. E. Rose. A S#lmbolic and Connectionist Approach to Legal Information Retrieval Lawrence Erlbaum, Hillsdale, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. 47 D. E. Rose and R. K. Belew. Legal information retrieval: A hybrid approach. In Proc of the lnt'l Conference on Artificial Intelligence and Law (ICAIL'89), Vancouver, Canada, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. 48 D. E. Rumelhaxt and D. Zipser. Feature discovery by competitive learning. In D. E. Rumelhart, J. L. Mc- Clelland, and the PDP Research Group, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. L Foundations. MIT Press, Cambridge, MA, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. 49 G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, MA, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. 50 E. Schweighofer, W. Winiwarter, and D. Merld. Information filtering: The computation of similarities in large corpora of legal text. In Proc of the {nt'l Conf on Artificial Intelligence and Law ({CA{L '95), College Park, MD, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. 51 K. Swingler. Applying Neural Networks: A Practical Guide. Academic Press, London, 1996.Google ScholarGoogle Scholar
  52. 52 H. R. Turtle and W. B. Croft. A comparison of text retrieval models. Computer Journal, 35(3), 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. 53 A. Ultsch. Self-organizing neural networks for visualization and classification. In O. Opitz, B. Lausen, and R. Klar, editors, Information and Classification- Concepts, Methods, and Applications. Springer-Verlag, Berlin, 1993.Google ScholarGoogle Scholar
  54. 54 R. Wilkinson and P. Hingston. Incorporating the vector space model in a neural network used for information retrieval. In Proc of the A CM SIGIR lnt7 Conf on Research and Development in Information Retrieval (SIGIR'91), Chicago, IL, 1991.Google ScholarGoogle Scholar
  55. 55 P. Willet. Reeend trends in hierarchic document clustering: A critical review. Information Processing #1 Management, 24, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Exploration of text collections with hierarchical feature maps

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in

            Full Access

            • Published in

              cover image ACM SIGIR Forum
              ACM SIGIR Forum  Volume 31, Issue SI
              Special issue of the SIGIR forum
              December 1997
              315 pages
              ISSN:0163-5840
              DOI:10.1145/278459
              Issue’s Table of Contents
              • cover image ACM Conferences
                SIGIR '97: Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
                July 1997
                348 pages
                ISBN:0897918363
                DOI:10.1145/258525

              Copyright © 1997 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 1 July 1997

              Check for updates

              Qualifiers

              • article

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader