Grounded models of semantic representation

EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

Volumn , Issue , 2012, Pages 1423-1433

  Silberer, Carina ^a   Lapata, Mirella ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPARATIVE STUDIES; LATENT INFORMATION; LINGUISTIC ENVIRONMENT; LINGUISTIC INFORMATION; NATURALLY OCCURRING; PERCEPTION AND ACTIONS; SEMANTIC KNOWLEDGE; SEMANTIC REPRESENTATION;

LINGUISTICS; SEMANTICS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84883376937     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. M. Andrews, G. Vigliocco, and D. Vinson. 2009. Integrating Experiential and Distributional Data to Learn Semantic Representations. Psychological Review, 116(3):463-498.
2. Lawrence Barsalou. 1999. Perceptual Symbol Systems. Behavioral and Brain Sciences, 22:577-609.
3. Lawrence W. Barsalou. 2008. Grounded Cognition. Annual Review of Psychology, 59:617-845.
4. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993-1022, March.
5. Magnus Borga. 2001. Canonical Correlation - a Tutorial, January.
6. M. H. Bornstein, L. R. Cote, S. Maital, K. Painter, S.-Y. Park, and L. Pascual. 2004. Cross-linguistic Analysis of Vocabulary in Young Children: Spanish, Dutch, French, Hebrew, Italian, Korean, and American English. Child Development, 75(4):1115-1139.
7. Elia Bruni, Giang Binh Tran, and Marco Baroni. 2011. Distributional Semantics from Text and Images. In Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, pages 22-32, Edinburgh, UK, July. Association for Computational Linguistics.
8. Freddy Choi, Peter Wiemer-Hastings, and Johanna Moore. 2001. Latent Semantic Analysis for Text Segmentation. In Proceedings of the 6th EMNLP, pages 109-117, Seattle, WA.
9. J Cohen and P Cohen. 1983. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, NJ: Erlbaum.
10. Yansong Feng and Mirella Lapata. 2010. Visual Information in Semantic Representation. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 91-99, Los Angeles, California, June. Association for Computational Linguistics.
11. Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2002. Placing Search in Context: The Concept Revisited. ACM Transactions on Information Systems, 20(1):116-131, January.
12. Arthur M. Glenberg and Michael P. Kaschak. 2002. Grounding Language in Action. Psychonomic Bulletin and Review, 9(3):558-565.
13. Gregory Grefenstette. 1994. Explorations in Automatic Thesaurus Discovery. Kluwer Academic Publishers.
14. T. L. Griffiths, M. Steyvers, and J. B. Tenenbaum. 2007. Topics in Semantic Representation. Psychological Review, 114(2):211-244.
15. David R. Hardoon, Sandor R. Szedmak, and John R. Shawe-Taylor. 2004. Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Computation, 16(12):2639-2664.
16. H Hotelling. 1936. Relations between Two Sets of Vari-ates. Biometrika, 28:312-377.
17. Steve R. Howell, Damian Jankowicz, and Suzanna Becker. 2005. A Model of Grounded Language Acquisition: Sensorimotor Features Improve Lexical and Grammatical Learning. Journal of Memory and Language, 53(2), 258-276, 53(2):258-276.
18. Brendan T. Johns and Michael N. Jones. 2012. Perceptual Inference through Global Lexical Similarity. Topics in Cognitive Science, 4(1):103-120.
19. B. Landau, L. Smith, and S. Jones. 1998. Object Perception and Object Naming in Early Development. Trends in Cognitive Science, 27:19-24.
20. T. Landauer and S. T. Dumais. 1997. A Solution to Plato's Problem: the Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. Psychological Review, 104(2):211-240.
21. Dekang Lin. 1998. Automatic Retrieval and Clustering of Similar Words. In Proceedings of the joint Annual Meeting of the Association for Computational Linguistics and International Conference on Computational Linguistics, pages 768-774, Montréal, Canada.
22. K. McRae, G. S. Cree, M. S. Seidenberg, and C. McNorgan. 2005. Semantic Feature Production Norms for a Large Set of Living and Nonliving Things. Behavior Research Methods, 37(4):547-559, November.
23. D. L. Nelson, C. L. McEvoy, and T. A. Schreiber. 1998. The University of South Florida Word Association, Rhyme, and Word Fragment Norms.
24. C. Perfetti. 1998. The Limits of Co-occurrence: Tools and Theories in Language Research. Discourse Processes, (25):363-377.
25. Terry Regier. 1996. The Human Semantic Potential. MIT Press, Cambridge, MA.
26. T. T. Rogers, M. A. Lambon Ralph, P. Garrard, S. Bozeat, J. L. McClelland, J. R. Hodges, and K. Patterson. 2004. Structure and Deterioration of Semantic Memory: A Neuropsychological and Computational Investigation. Psychological Review, 111(1):205-235.
27. G Salton, A Wang, and C Yang. 1975. A Vector-space Model for Information Retrieval. Journal of the American Society for Information Science, 18:613-620.
28. Hinrich Schütze. 1998. Automatic Word Sense Discrimination. Computational Linguistics, 24(1):97-124.
29. Mark Steyvers. 2010. Combining Feature Norms and Text Data with Topic Models. Acta Psychologica, 133(3):234-342.
30. Wouter Voorspoels, Wolf Vanpaemel, and Gert Storms. 2008. Exemplars and Prototypes in Natural Language Concepts: A Typicality-based Evaluation. Psychonomic Bulletin & Review, 15:630-637.