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A Vector Field Approach to Lexical Semantics

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Quantum Interaction (QI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8951))

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Abstract

We report work in progress on measuring “forces” underlying the semantic drift by comparing it with plate tectonics in geology. Based on a brief survey of energy as a key concept in machine learning, and the Aristotelian concept of potentiality vs. actuality allowing for the study of energy and dynamics in language, we propose a field approach to lexical analysis. Until evidence to the contrary, it was assumed that a classical field in physics is appropriate to model word semantics. The approach used the distributional hypothesis to statistically model word meaning. We do not address the modelling of sentence meaning here. The computability of a vector field for the indexing vocabulary of the Reuters-21578 test collection by an emergent self-organizing map suggests that energy minima as learnables in machine learning presuppose concepts as energy minima in cognition. Our finding needs to be confirmed by a systematic evaluation.

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References

  1. Antoniou, G., d’Aquin, M., Pan, J.Z.: Semantic web dynamics. Web Seman. Sci. Serv. Agents World Wide Web 9, 245–246 (2011)

    Article  Google Scholar 

  2. Lauriston, A.: Criteria for measuring term recognition. In: Proceedings of EACL-95, 7th Conference of the European Chapter of the Association for Computational Linguistics, pp. 17–22 (1995)

    Google Scholar 

  3. Gulla, J.A., Solskinnsbakk, G., Myrseth, P., Haderlein, V., Cerrato, O.: Concept signatures and semantic drift. In: Filipe, J., Cordeiro, J. (eds.) WEBIST 2010. LNBIP, vol. 75, pp. 101–113. Springer, Berlin (2011)

    Chapter  Google Scholar 

  4. Delany, S.J., Cunningham, P., Tsymbal, A., Coyle, L.: A case-based technique for tracking concept drift in spam filtering. Knowl. Based Syst. 18, 187–195 (2005)

    Article  Google Scholar 

  5. Wang, S., Schlobach, S., Klein, M.: Concept drift and how to identify it. Web Seman. Sci. Serv. Agents World Wide Web 9, 247–265 (2011)

    Article  Google Scholar 

  6. Ross, G.J., Adams, N.M., Tasoulis, D.K., Hand, D.J.: Exponentially weighted moving average charts for detecting concept drift. Pattern Recogn. Lett. 33, 191–198 (2012)

    Article  Google Scholar 

  7. Gonçalves Jr., P.M., Barros, R.S.M.: Rcd: A recurring concept drift framework. Pattern Recogn. Lett. 34, 1018–1025 (2013)

    Article  Google Scholar 

  8. Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010)

    MATH  MathSciNet  Google Scholar 

  9. Padó, S., Lapata, M.: Dependency-based construction of semantic space models. Comput. Linguist. 33, 161–199 (2007)

    Article  MATH  Google Scholar 

  10. Erk, K., Padó, S.: A structured vector space model for word meaning in context. In: Proceedings of EMNLP-08, 13th Conference on Empirical Methods in Natural Language Processing, pp. 897–906. (2008)

    Google Scholar 

  11. Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of EMNLP-CoNLL-12, Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1201–1211 (2012)

    Google Scholar 

  12. Baroni, M., Lenci, A.: Distributional memory: a general framework for corpus-based semantics. Comput. Linguist. 36, 673–721 (2010)

    Article  Google Scholar 

  13. Blacoe, W., Kashefi, E., Lapata, M.: A quantum-theoretic approach to distributional semantics. In: Proceedings of NAACL-HLT-13, Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 847–857 (2013)

    Google Scholar 

  14. Grefenstette, E., Dinu, G., Zhang, Y.Z., Sadrzadeh, M., Baroni, M.: Multi-step regression learning for compositional distributional semantics (2013). arXiv:1301.6939

  15. Cohen, T., Widdows, D., Schvaneveldt, R.W., Rindflesch, T.C.: Discovery at a distance: farther journeys in predication space. In: Proceedings of BIBMW-12, IEEE International Conference on Bioinformatics and Biomedicine Workshops, pp. 218–225 (2012)

    Google Scholar 

  16. Erk, K., McCarthy, D., Gaylord, N.: Measuring word meaning in context. Comput. Linguist. 39, 511–554 (2013)

    Article  Google Scholar 

  17. Elman, J.L.: An alternative view of the mental lexicon. Trends Cogn. Sci. 8, 301–306 (2004)

    Article  Google Scholar 

  18. Fodor, J.A.: The Language of Thought, vol. 5. Harvard University Press, Massachusetts (1975)

    Google Scholar 

  19. House, J.: Linguistic relativity and translation. Amsterdam Stud. Theory Hist. Linguist. Sci. 4, 69–88 (2000)

    Google Scholar 

  20. Trier, J.: Das sprachliche feld. Neue Jahrbucher fur Wissenschaft und Jugendbildung 10, 428–449 (1934)

    Google Scholar 

  21. De Saussure, F.: Course in General Linguistics. Columbia University Press, New York (2011)

    Google Scholar 

  22. Kožnjak, B.: Möglichkeit, wirklichkeit und quantenmechanik. Prolegomena 6, 223–252 (2007)

    Google Scholar 

  23. Bohm, D.: Quantum Theory. Dover Publications, New York (1989)

    Google Scholar 

  24. Heisenberg, W.: Physics and Philosophy: The Revolution of Modern Science. Harper & Row, New York (1958)

    Google Scholar 

  25. Aerts, D., Gabora, L.: A theory of concepts and their combinations I: the structure of the sets of contexts and properties. Kybernetes 34, 151–175 (2005)

    Article  Google Scholar 

  26. Wittek, P., Darányi, S.: Spectral composition of semantic spaces. In: Song, D., Melucci, M., Frommholz, I., Zhang, P., Wang, L., Arafat, S. (eds.) QI 2011. LNCS, vol. 7052, pp. 60–70. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  27. Darányi, S., Wittek, P.: Connecting the dots: mass, energy, word meaning, and particle-wave duality. In: Busemeyer, J.R., Dubois, F., Lambert-Mogiliansky, A., Melucci, M. (eds.) QI 2012. LNCS, vol. 7620, pp. 207–217. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  28. Mihalcea, R., Moldovan, D.I.: Word sense disambiguation based on semantic density. In: Proceedings of COLING-ACL, 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics (1998)

    Google Scholar 

  29. Melucci, M.: Initial specifications for the design of information retrieval systems based on quantum detector using kinds. In: Atmanspacher, H., Haven, E., Kitto, K., Raine, D. (eds.) QI 2013. LNCS, pp. 59–70. Springer, Berlin (2013)

    Google Scholar 

  30. Darányi, S., Wittek, P.: Demonstrating conceptual dynamics in an evolving text collection. J. Am. Soc. Inf. Sci. Technol. 64, 2564–2572 (2013)

    Article  Google Scholar 

  31. Weinstein, M., Horn, D.: Dynamic quantum clustering: a method for visual exploration of structures in data. Phys. Rev. E 80, 066117 (2009)

    Article  Google Scholar 

  32. Neven, H., Denchev, V.S., Drew-Brook, M., Zhang, J., Macready, W.G., Rose, G.: Binary classification using hardware implementation of quantum annealing. In: Demonstrations at NIPS-09, 24th Annual Conference on Neural Information Processing Systems, pp. 1–17 (2009)

    Google Scholar 

  33. Trugenberger, C.A.: Probabilistic quantum memories. Phys. Rev. Lett. 87, 067901 (2001)

    Article  Google Scholar 

  34. Amit, D.J.: Modeling Brain Function: The World of Attractor Neural Networks. Cambridge University Press, Cambridge (1992)

    Google Scholar 

  35. Falissard, B.: A thought experiment reconciling neuroscience and psychoanalysis. J. Physiol Paris 105, 201–206 (2011)

    Article  Google Scholar 

  36. Just, M.A., Cherkassky, V.L., Aryal, S., Mitchell, T.M.: A neurosemantic theory of concrete noun representation based on the underlying brain codes. PLoS ONE 5, e8622 (2010)

    Article  Google Scholar 

  37. Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (2001)

    Book  MATH  Google Scholar 

  38. Ultsch, A., Mörchen, F.: ESOM-maps: tools for clustering, visualization, and classification with emergent SOM. Technical report. Data Bionics Research Group, University of Marburg (2005)

    Google Scholar 

  39. Wittek, P.: Somoclu: an efficient distributed library for self-organizing maps (2013). arXiv:1305.1422

  40. Budanitsky, A., Hirst, G.: Evaluating WordNet-based measures of lexical semantic relatedness. Comput. Linguist. 32, 13–47 (2006)

    Article  MATH  Google Scholar 

  41. Zhang, Z., Gentile, A.L., Ciravegna, F.: Recent advances in methods of lexical semantic relatedness-a survey. Nat. Lang. Eng. 19, 411–479 (2013)

    Article  Google Scholar 

  42. Newman, D., Lau, J.H., Grieser, K., Baldwin, T.: Automatic evaluation of topic coherence. In: Proceedings of NAACL-HLT-10, Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, pp. 100–108 (2010)

    Google Scholar 

  43. Wittek, P., Ravenek, W.: Supporting the exploration of a corpus of 17th-century scholarly correspondences by topic modeling. In: Proceedings of SDH-11, Supporting Digital Humanities: Answering the Unaskable (2011)

    Google Scholar 

  44. Kievit-Kylar, B., Jones, M.N.: Visualizing multiple word similarity measures. Behav. Res. Meth. 44, 656–674 (2012)

    Article  Google Scholar 

  45. Weeds, J., Weir, D.: Co-occurrence retrieval: a flexible framework for lexical distributional similarity. Comput. Linguist. 31, 439–475 (2005)

    Article  MATH  Google Scholar 

  46. Rohde, D.L., Gonnerman, L.M., Plaut, D.C.: An improved model of semantic similarity based on lexical co-occurrence. Commun. ACM 8, 627–633 (2006)

    Google Scholar 

  47. Clarke, D.: A context-theoretic framework for compositionality in distributional semantics. Comput. Linguist. 38, 41–71 (2012)

    Article  Google Scholar 

  48. Bruni, E., Uijlings, J., Baroni, M., Sebe, N.: Distributional semantics with eyes: using image analysis to improve computational representations of word meaning. In: Proceedings of MM-12, 20th ACM International Conference on Multimedia, pp. 1219–1228 (2012)

    Google Scholar 

  49. Ursino, M., Cuppini, C., Magosso, E.: A computational model of the lexical-semantic system based on a grounded cognition approach. Embodied and Grounded Cognition 1 (2010)

    Google Scholar 

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Acknowledgement

The authors are grateful for the comments of three anonymous reviewers. Numerous suggestions from the audience of QI-14 helped to link our work to ongoing parallel research in the field. The current development phase of Somoclu was supported by the European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 PERICLES.

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Authors and Affiliations

  1. University of Borås, Borås, Sweden

    Peter Wittek & Sándor Darányi

  2. Charles Sturt University, Wagga Wagga, Australia

    Ying-Hsang Liu

Authors
  1. Peter Wittek
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  2. Sándor Darányi
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  3. Ying-Hsang Liu
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Corresponding author

Correspondence to Sándor Darányi .

Editor information

Editors and Affiliations

  1. ETH Zürich, Zürich, Switzerland

    Harald Atmanspacher

  2. Universitätsspital Bern, Bern, Switzerland

    Claudia Bergomi

  3. University of Freiburg, Freiburg, Germany

    Thomas Filk

  4. Queensland University of Technology, Brisbane, Queensland, Australia

    Kirsty Kitto

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Wittek, P., Darányi, S., Liu, YH. (2015). A Vector Field Approach to Lexical Semantics. In: Atmanspacher, H., Bergomi, C., Filk, T., Kitto, K. (eds) Quantum Interaction. QI 2014. Lecture Notes in Computer Science(), vol 8951. Springer, Cham. https://doi.org/10.1007/978-3-319-15931-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-15931-7_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-15931-7

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