Skip to main content
SpringerLink
Log in

Semantic Representation and Priming in a Self-organizing Lexicon

  • Conference paper
4th Neural Computation and Psychology Workshop, London, 9–11 April 1997

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

This paper presents a model of the mental lexicon and its formation, based on the self-organizing neural network. When exposed to raw text, the model clusters words according to their semantic relatedness to form a semantic network [7]. Simulations using artificial data are described that show how co-occurrence information can be used to create a low-dimensional representation of lexical semantics. The model also suggests a novel explanation of semantic priming based on topographic organization and co-occurrence statistics. A simulation that implements this explanation is presented, and the model is tested against priming effects found in Moss et al.’s [25] semantic priming experiment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. J. R. Anderson. The Architecture of Cognition. Harvard University Press, 1983.

    Google Scholar 

  2. C. M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.

    Google Scholar 

  3. J. A. Bullinaria and C. C. Huckle. Modelling lexical decision using corpus derived semantic representations in a connectionist network. In Proceedings of the Fourth Neural Computation and Psychology Workshop, 1997.

    Google Scholar 

  4. G. Burnage and D. Dunlop. Enoding the British National Corpus. In Papers from the Thirteenth International Conference on English Language Research on Computerized Corpora, 1992.

    Google Scholar 

  5. K. W. Church and P. Hanks. Word association, mutual information,and lexicography. Computational Linguistics, 1989.

    Google Scholar 

  6. K. W. Church and R. L. Mercer. Introduction to the special issue on computational linguistics using large corpora. Computational Linguistics, 19: 1–24, 1993.

    Google Scholar 

  7. A. M. Collins and E. F. Loftus. A spreading activation theory of semantic processing. Psychological Review, 82: 407–428, 1975.

    Article  Google Scholar 

  8. T. Dunning. Robust statistics for surprise and coincidence. Computational Linguistics, 1993.

    Google Scholar 

  9. J. L. Elman. Finding structure in time. Cognitive Science, 14: 179–211, 1990.

    Article  Google Scholar 

  10. S. Finch. Finding Structure in Language. PhD thesis, Centre for Cognitive Science, Edinburgh University, 1993.

    Google Scholar 

  11. C. C. Huckle. Unsupervised Categorization of Word Meanings Using Statistical and Neural Network Methods. PhD thesis, Centre for Cognitive Science, Edinburgh University, 1995.

    Google Scholar 

  12. E. I. Knudsen, S. du Lac, and S. D. Esterly. Computational maps in the brain. Annual Review of Neuroscience, 10: 41–65, 1988.

    Article  Google Scholar 

  13. T. Kohonen. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43: 59–69, 1982.

    Article  MATH  MathSciNet  Google Scholar 

  14. T. Kohonen. Self-organization and associative memory. Springer Verlag, 1984.

    Google Scholar 

  15. T. Kohonen. Physiological interpretation of the self-organizing map algorithm. Neural Computation, 6: 895–905, 1993.

    Google Scholar 

  16. T. Kohonen. Self-organizing maps. Springer Verlag, 1995.

    Google Scholar 

  17. T. K. Landauer and S. T. Dumais. A solution to Plato’s problem: the latent semantic analysis theory of induction and representation of knowledge. Psychological Review, 104: 211–240, 1997.

    Article  Google Scholar 

  18. Will Lowe. Meaning and the mental lexicon. In Proceeding of the 15th International Joint Conference on Artificial Intelligence, 1997.

    Google Scholar 

  19. K. Lund, C. Burgess, and R. A. Atchley. Semantic and associative priming in high-dimensional semantic space. In Proceedings of the 17th Annual Conference of the Cognitive Science Society, pages 660–665, 1995.

    Google Scholar 

  20. M. E. Masson. A distributed memory model of context effects in word identification. In D. Besner and G. W. Humphreys, editors, Basic Processes in Reading: Visual Word Recognition. Lawrence Erlbaum Associates, 1991.

    Google Scholar 

  21. J. McClelland and D. Rumelhart. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1. MIT Press, 1988.

    Google Scholar 

  22. S. McDonald. A context-based model of semantic similarity. Unpublished.

    Google Scholar 

  23. S. Monsell. The nature and locus of word frequency effects in reading. In D. Besner and G. W. Humphreys, editors, Basic Processes in Reading: Visual Word Recognition. Lawrence Erlbaum Associates, 1991.

    Google Scholar 

  24. J. Morton. Word recognition. In J. Morton and J. C. Marshall, editors, Psycholinguistics Series 2: Structures and Processes. Elek, 1979.

    Google Scholar 

  25. H. E. Moss, R. K. Ostrin, L. K. Tyler, and W. D. Marslen-Wilson. Accessing different types of lexical semantic information: Evidence from priming. Journal of Experimental Psychology: Learning, Memory and Cognition, pages 863–883, 1995.

    Google Scholar 

  26. J. H. Neely. Semantic priming effects in visual word recognition: A selective review of current findings and theories. In D. Besner and G. W. Humphreys, editors Basic Processes in Reading: Visual Word Recognition. Lawrence Erlbaum Associates, 1991.

    Google Scholar 

  27. D. I. Perrett. View-dependent coding in the ventral stream and its consequences for recognition. In R. Caminiti, K-P Hoffmann, F. Lacquaniti, and J. Altman, editors Vision and Movement: Mechanisms in the Cerebral Cortexvolume 2 of HFSPpages 142–151. Publisher, Strasbourg, 1996.

    Google Scholar 

  28. D. C. Plant. Semantic and associative priming in a distributed attractor network. In Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society1995.

    Google Scholar 

  29. F. Pulvermüller and H. Preissl. A cell assembly model of language. Network: Computation in Neural Systemspages 455–468, 1991.

    Google Scholar 

  30. H. Schütze. Word space. In S. J. Hanson, J. D. Cowan, and C. L. Giles, editors Advances in Neural Information Processing Systemsvolume 5, pages 895–902. Morgan Kaufmann, 1993.

    Google Scholar 

  31. M. S. Seidenberg. Visual word recognition and pronunciation: A computational model and its implications. In W. Marslen-Wilson, editor Lexical Representation and Processchapter 2. MIT Press, 1989.

    Google Scholar 

  32. J. R. Shelton and R. C. Martin. How semantic is automatic semantic priming? Journal of Experimental Psychology: Learning Memory and Cognition18:1191–1210, 1992.

    Google Scholar 

  33. J. Sirosh and R. Miikkulainen. How lateral interaction develops a self-organizing feature map. In Proceedings of the IEEE International Conference on Neural Networks1993.

    Google Scholar 

  34. D. P. Spence and K. C. Owens. Lexical co-occurrence and association strength. Journal of Psycholinguistic Research19:317–330, 1990.

    Google Scholar 

  35. M. Taft. Reading and the Mental Lexicon. Lawrence Erlbaum Associates, 1991.

    Google Scholar 

  36. K. Tanaka. Inferotemporal cortex and object recognition. In R. Caminiti, K-P Hoffmann, F. Lacquaniti, and J. Altman, editors Vision and Movement: Mechanisms in the Cerebral Cortexvolume 2 of HFSPpages 126133. Publisher, Strasbourg, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Cognitive Science, Edinburgh University, Edinburgh, Scotland

    Will Lowe

Authors
  1. Will Lowe
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centre for Speech and Language, Department of Psychology, Birkbeck College, University of London, Malet Street, WC1E 7HX, London, UK

    John A. Bullinaria BSc, MSc, PhD

  2. Department of Psychology, University College London, Gower Street, WC1E 6BT, London, UK

    David W. Glasspool BSc, Msc  & George Houghton BA, MSc, PhD  & 

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag London Limited

About this paper

Cite this paper

Lowe, W. (1998). Semantic Representation and Priming in a Self-organizing Lexicon. In: Bullinaria, J.A., Glasspool, D.W., Houghton, G. (eds) 4th Neural Computation and Psychology Workshop, London, 9–11 April 1997. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1546-5_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-1546-5_18

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76208-9

  • Online ISBN: 978-1-4471-1546-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation