Abstract
Word space models are used to encode the semantics of natural language elements by means of high dimensional vectors [23]. Latent Semantic Analysis (LSA) methodology [15] is well known and widely used for its generalization properties. Despite of its good performance in several applications, the model induced by LSA ignores dynamic changes in sentences meaning that depend on the order of the words, because it is based on a bag of words analysis. In this chapter we present a technique that exploits LSA-based semantic spaces and geometric algebra in order to obtain a sub-symbolic encoding of sentences taking into account the words sequence in the sentence.
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References
Agostaro, F., Pilato, G., Vassallo, G., Gaglio, S.: A sub-symbolic approach to word modelling for domain specific speech recognition. In: Proceedings of IEEE CAMP. International Workshop on Computer Architecture for Machine Perception, pp. 321–326 (2005)
Annesi, P., Storch, V., Basili, R.: Space projections as distributional models for semantic composition. In: Proceedings of the 13th International Conference on Computational Linguistics and Intelligent Text Processing - Volume Part I, CICLing’12, pp. 323–335. Springer, Heidelberg (2012)
Artin, E.: Geometric Algebra. Interscience Tracts in Pure and Applied Mathematics. Wiley, New York (1988)
Augello, A., Vassallo, G., Gaglio, S., Pilato, G.: Sentence induced transformations in "conceptual" spaces. In: IEEE Computer Society on Proceedings of the 2008 IEEE International Conference on Semantic Computing, ICSC ’08, pp. 34–41. Washington, USA (2008)
Baroni, M., Bernardini, S., Ferraresi, A., Zanchetta, E.: The wacky wide web: a collection of very large linguistically processed web-crawled corpora. Lang. Resour. Eval. 43, 209–226 (2009)
Basile, P., Caputo, A., Semeraro. G.: A study on compositional semantics of words in distributional spaces. In: 2012 IEEE Sixth International Conference on Semantic Computing (ICSC), pp. 154–161
Budiu, R., Royer, C., Pirolli, P.: Modeling information scent: a comparison of lsa, pmi and glsa similarity measures on common tests and corpora. In: Large Scale Semantic Access to Content (Text, Image, Video, and Sound), RIAO ’07, pp. 314–332. Paris, France (2007). Le Centre de Hautes Etudes Internationales d’informatique documentaire
Christopher, D.: Manning and Hinrich Schütze. Foundations of statistical natural language processing. MIT Press, USA (1999)
Clark, Stephen, Coecke, Bob, Sadrzadeh, Mehrnoosh: Mathematical foundations for a compositional distributed model of meaning. Linguist. Anal. 36(1–4), 345–384 (2011)
David, J.S.: Handbook of Parametric and Nonparametric Statistical Procedures. 3rd edn. Chapman & Hall/CRC (2003)
Dowty, D.R., Wall, R.E., Peters, S.: Introduction to Montague Semantics. vol. 11. D. Reidel, Dordrecht (1989)
Edward Grefenstette. Compositionality in distributional semantic models: A critical overview of current literature (2010)
GEMS Shared Evaluation. In: Proceedings of the GEMS 2011 Workshop on Geometrical Models of Natural Language Semantics, Association for Computational Linguistics. Edinburgh, UK (2011)
Hinrich, S.: Word space. In: Giles, L.C., Hanson, S.J., Cowan, J.D. (eds.) Advances in Neural Information Processing Systems 5, pp. 895–902. Morgan Kaufmann, San Francisco, CA (1993)
Landauer, T.K., Dumais, S.T.: Solutions to plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104 (1997)
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25, 259–284 (1998)
Lounesto, P.: Clifford Algebra and Spinors. Cambridge University Press, Cambridge (1997)
Mitchell, J., Lapata, M.: Vector-based models of semantic composition. In: Proceedings of ACL-08: HLT, pp. 236–244. Columbus, Ohio (2008)
Mitchell, Jeff, Lapata, Mirella: Composition in distributional models of semantics. Cogn. Sci. 34(8), 1388–1429 (2010)
Pilato, G., Augello, A., Gentile, M., Vassallo, G.: Geometric encoding of sentences based on clifford algebra. In: IC3K - KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Special Session on Information Filtering and Retrieval: Novel Distributed Systems and Applications (DART). SciTePress, Barcelona (2012)
Rubenstein, Herbert, Goodenough, John B.: Contextual correlates of synonymy. Commun. ACM 8(10), 627–633 (October 1965)
Sahlgren, M.: The distributional hypothesis. From context to meaning: Distributional models of the lexicon in linguistics and cognitive science (Special issue of the Italian Journal of Linguistics) 20(1), 33–53 (2008)
Sahlgren, M.: The Word-Space Model. Ph.D. thesis, Stockholm University (2006). http://www.sics.se/mange/TheWordSpaceModel.pdf
Schoute, P.H.: Mehrdimensionale geometrie. Leipzig: G.J.Gschensche Verlagsha (Sammlung Schubert XXXV): Die linearen Rume, 1, 1902
Schütze, Hinrich: Automatic word sense discrimination. Comput. Linguist. 24(1), 97–123 (March 1998)
Acknowledgments
We are grateful to Professor Thomas Landauer, to Praful Mangalath and the Institute of Cognitive Science of the University of Colorado Boulder for providing us the TASA corpus. This work has been partially supported by the PON01_01687—SINTESYS (Security and INTElligence SYSstem) Research Project.
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Augello, A., Gentile, M., Pilato, G., Vassallo, G. (2014). A Geometric Algebra Based Distributional Model to Encode Sentences Semantics. In: Lai, C., Giuliani, A., Semeraro, G. (eds) Distributed Systems and Applications of Information Filtering and Retrieval. Studies in Computational Intelligence, vol 515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40621-8_6
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