Multi-step regression learning for compositional distributional semantics

Proceedings of the 10th International Conference on Computational Semantics, IWCS 2013 - Long Papers

Volumn , Issue , 2013, Pages

  Grefenstette, E ^a   Dinu, G ^b   Zhang, Y ^c   Sadrzadeh, M ^a   Baroni, M ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF TRENTO   (Italy)

^c UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; SEMANTICS; TENSORS;

BENCHMARK DATA; DISTRIBUTIONAL MODELS; DISTRIBUTIONAL SEMANTICS; FORMAL SEMANTICS; LEARNING METHODS; MULTI-STEP;

FORMAL METHODS;

EID: 84943741021     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Baroni, M. and R. Zamparelli (2010). Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space. In Proceedings of EMNLP, Boston, MA, pp. 1183-1193.
2. Bourbaki, N. (1989). Commutative Algebra: Chapters 1-7. Springer-Verlag (Berlin and New York).
3. Bullinaria, J. and J. Levy (2012). Extracting semantic representations from word co-occurrence statistics: Stop-lists, stemming and SVD. Behavior Research Methods 44, 890-907.
4. Coecke, B., M. Sadrzadeh, and S. Clark (2010). Mathematical foundations for a compositional distributional model of meaning. Linguistic Analysis 36, 345-384.
5. Dinu, G. and M. Lapata (2010). Measuring distributional similarity in context. In Proceedings of EMNLP, Cambridge, MA, pp. 1162-1172.
6. Dumais, S. (2003). Data-driven approaches to information access. Cognitive Science 27, 491-524.
7. Erk, K. and S. Padó (2008). A structured vector space model for word meaning in context. In Proceedings of EMNLP, Honolulu, HI, USA, pp. 897-906.
8. Firth, J. (1957). Papers in linguistics, 1934-1951. Oxford University Press.
9. Frege, G. (1892). Ü ber Sinn und Bedeutung. Zeitschrift fuer Philosophie un philosophische Kritik 100, 25-50.
10. Golub, G., M. Heath, and G. Wahba (1979). Generalized cross-validation as a method for choosing a good Ridge parameter. Technometrics 21, 215-223.
11. Grefenstette, E. and M. Sadrzadeh (2011a). Experimental support for a categorical compositional distributional model of meaning. In Proceedings of EMNLP, Edinburgh, UK, pp. 1394-1404.
12. Grefenstette, E. and M. Sadrzadeh (2011b). Experimenting with transitive verbs in a DisCoCat. In Proceedings of GEMS, Edinburgh, UK, pp. 62-66.
13. Grefenstette, E., M. Sadrzadeh, S. Clark, B. Coecke, and S. Pulman (2011). Concrete sentence spaces for compositional distributional models of meaning. In Proceedings of IWCS, pp. 125-134.
14. Grefenstette, G. (1994). Explorations in Automatic Thesaurus Discovery. Boston, MA: Kluwer.
15. Guevara, E. (2010). A regression model of adjective-noun compositionality in distributional semantics. In Proceedings of the ACL GEMS Workshop, Uppsala, Sweden, pp. 33-37.
16. Hastie, T., R. Tibshirani, and J. Friedman (2009). The Elements of Statistical Learning, 2nd ed. New York: Springer.
17. Landauer, T. and S. 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.
18. Lee, J. (1997). Riemannian manifolds: An introduction to curvature, Volume 176. Springer Verlag.
19. Lin, C.-J. (2007). Projected gradient methods for nonnegative matrix factorization. Neural Computation 19(10), 2756-2779.
20. Mitchell, J. and M. Lapata (2008). Vector-based models of semantic composition. In Proceedings of ACL, Columbus, OH, pp. 236-244.
21. Mitchell, J. and M. Lapata (2010). Composition in distributional models of semantics. Cognitive Science 34(8), 1388-1429.
22. Montague, R. (1970). English as a formal language. Linguaggi nella società e nella tecnica, 189-224.
23. Partee, B. (2004). Compositionality in Formal Semantics. Malden, MA: Blackwell.
24. Sahlgren, M. (2006). The Word-Space Model. Dissertation, Stockholm University.
25. Schütze, H. (1997). Ambiguity Resolution in Natural Language Learning. Stanford, CA: CSLI.
26. Socher, R., B. Huval, C. Manning, and A. Ng (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of EMNLP, Jeju Island, Korea, pp. 1201-1211.
27. Thater, S., H. Fürstenau, and M. Pinkal (2011). Word meaning in context: A simple and effective vector model. In Proceedings of IJCNLP, Chiang Mai, Thailand, pp. 1134-1143.
28. Wittgenstein, L. (1953). Philosophical Investigations. Oxford: Blackwell. Translated by G. E. M. Anscombe.