Semantic Addressable Encoding

  • Cheng-Yuan Liou
  • Jau-Chi Huang
  • Wen-Chie Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


This paper presents an automatic acquisition process to acquire the semantic meaning for the words. This process obtains the representation vectors for stemmed words by iteratively improving the vectors, using a trained Elman network [4]. Experiments performed on a corpus composed of Shakespeare’s writings show its linguistic analysis and categorization abilities.

Index Terms: word perception, authorship, categorization, semantic search, Elman network, linguistic analysis, personalized code, content addressable memory.


Hide Layer Semantic Meaning Linguistic Analysis Word Sequence Semantic Search 


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  1. 1.
    Bloom, H.: Shakespeare: The Invention of Human. Riverhead Books, New York (1998)Google Scholar
  2. 2.
    Burrows, J.: Questions of Athorship: Attribution and Beyond a Lecture Delivered on the Occasion of The Roberto Busa Award ACH-ALLC 2001. Computers and the humanities 37, 5–32 (2003)CrossRefGoogle Scholar
  3. 3.
    Elman, J.L., Bates, E.A., Johnson, M.H., Karmiloff-Smith, A., Parisi, D., Plunkett, K.: Rethink Innateness. The MIT Press, Cambridge (1996)Google Scholar
  4. 4.
    Elman, J.L.: Generalization, Simple Recurrent Networks, and the Emergence of Structure. In: The 20th Annual Conference of the Cognitive Science Society in Mahway, New Jeresy (1998)Google Scholar
  5. 5.
    Felsenstein, J.: PHYLIP (Phylogeny Inference Package) version 3.5c [Program]. Department of Genetics, University of Washington, Seattle (1993)Google Scholar
  6. 6.
    Frakes, W.B.: Stemming Algorithms. In: Frakes, W.B., Ricardo, B.-Y. (eds.) Information Retrieval: Data Structures and Algorithms, pp. 131–160. Prentice-Hall, Englewood Cliffs (1992)Google Scholar
  7. 7.
    Holmes, D.I.: The Evolution of Stylometry in Humanities Scholarship. Literary and Linguistic Computing 13, 111 (1998)CrossRefGoogle Scholar
  8. 8.
    Huffman, D.A.: A Method for the Construction of Minimum-redundancy Codes. Proceedings of the I.R.E. 40, 1098–1102 (1952)CrossRefGoogle Scholar
  9. 9.
    Jordan, M.I.: Serial Order: a Parallel Distributed Processing Approach. Cognitive Science Institute Tech. Rep. 8604, San Diego (1986)Google Scholar
  10. 10.
    Kohonen, T.: Clustering, Taxonomy, and Topological Maps of Patterns. In: Proceedings of the Sixth Int’l. Conference on Pattern Recognition in Silver Spring, pp. 114–125 (1982)Google Scholar
  11. 11.
    Lee, D.D., Seung, H.S.: Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  12. 12.
    Liou, C.-Y., Yu, W.-J.: Ambiguous Binary Representation in Multilayer Neural Network. In: Proceedings of Int’l. Conference on Neural Networks (ICNN), Perth, Australia, vol. 1, pp. 379–384 (1995)Google Scholar
  13. 13.
    Liou, C.-Y., Huang, J.-C., Kuo, Y.-T.: Geometrical Perspective on Learning Behavior. Journal of Information Science and Engineering 21, 721–732 (2005)MathSciNetGoogle Scholar
  14. 14.
    Liou, C.-Y., Lin, S.-L.: Finite Memory Loading in Hairy Neurons. Natural Computing 5(1), 15–42 (2006)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Liou, C.-Y.: Backbone Structure of Hairy Memory. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006, Part 1. LNCS, vol. 4131, pp. 688–697. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    McEnery, T., Oakes, M.: Authorship Identification and Computational Stylometry. In: Handbook of Natural Language Processing, pp. 545–562. Marcel Dekker, Inc., New York (2000)Google Scholar
  17. 17.
    Porter, M.F.: An Algorithm for Suffix Stripping. Program 14, 130–137 (1980)Google Scholar
  18. 18.
    Rumelhart, D.E., McClelland, J.L. (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press, Cambridge (1986)Google Scholar
  19. 19.
    Saitou, N., Nei, M.: The Neighbor-Joining Method: a New Method for Reconstructing Phylogenetic Trees. Molecular biology and evolution 4, 406–425 (1987)Google Scholar
  20. 20.
    Tweedie, F.J., Baayen, R.H.: How Variable a Constant be Measures of Lexical Richness in Perspective? Computers and the Humanities 32, 323–352 (1998)CrossRefGoogle Scholar
  21. 21.
    William, C.B.: Mendenhall’s Studies of Word-Length Distribution in the Works of Shakespeare and Bacon. Biometrika 62, 207–212 (1975)CrossRefGoogle Scholar
  22. 22.
    Yang, C.-C., Peng, C.-K., Yien, H.-W., Goldberger, A.L.: Information Categorization Approach to Literary Authorship Disputes. Physica A 329, 473–483 (2003)MATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Yoshida, N., Kiyoki, Y., Kitagawa, T.: An Associative Search Method Based on Symbolic Filtering and Semantic Ordering for Database Systems. In: Proceedings of 7th IFIP 2.6 Working Conference on Database Semantics (DS-7), Leysin, Switzerland, pp. 215–237 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cheng-Yuan Liou
    • 1
  • Jau-Chi Huang
    • 1
  • Wen-Chie Yang
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Taiwan University 

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