Separating disambiguation from composition in distributional semantics

CoNLL 2013 - 17th Conference on Computational Natural Language Learning, Proceedings

Volumn , Issue , 2013, Pages 114-123

  Kartsaklis, Dimitri ^a   Sadrzadeh, Mehrnoosh ^b   Pulman, Stephen ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

SEMANTICS;

COMPOSITE REPRESENTATIONS; DISTRIBUTIONAL MODELS; DISTRIBUTIONAL SEMANTICS; GENERIC PROCEDURES; PROOF OF CONCEPT; VECTOR REPRESENTATIONS; WHOLE PROCESS; WORD SENSE DISAMBIGUATION;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84906926412     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Baroni, M. and Zamparelli, R. (2010). Nouns are Vectors, Adjectives are Matrices. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP).
2. Blacoe, W. and Lapata, M. (2012). A comparison of vector-based representations for semantic composition. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 546–556, Jeju Island, Korea. Association for Computational Linguistics.
3. Broda, B. and Mazur, W. (2012). Evaluation of clustering algorithms for word sense disambiguation. International Journal of Data Analysis Techniques and Strategies, 4(3):219–236.
4. Caliński, T. and Harabasz, J. (1974). A Dendrite Method for Cluster Analysis. Communications in Statistics-Theory and Methods, 3(1):1–27.
5. Coecke, B., Sadrzadeh, M., and Clark, S. (2010). Mathematical Foundations for Distributed Compositional Model of Meaning. Lambek Festschrift. Linguistic Analysis, 36:345–384.
6. Erk, K. and Padó, S. (2008). A Structured Vector-Space Model for Word Meaning in Context. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 897–906.
7. Grefenstette, E., Dinu, G., Zhang, Y.-Z., Sadrzadeh, M., and Baroni, M. (2013). Multistep regression learning for compositional distributional semantics.
8. Grefenstette, E. and Sadrzadeh, M. (2011a). Experimental Support for a Categorical Compositional Distributional Model of Meaning. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP).
9. Grefenstette, E. and Sadrzadeh, M. (2011b). Experimenting with Transitive Verbs in a DisCo-Cat. In Proceedings of Workshop on Geometrical Models of Natural Language Semantics (GEMS).
10. Guevara, E. (2010). A Regression Model of Adjective-Noun Compositionality in Distributional Semantics. In Proceedings of the ACL GEMS Workshop.
11. Kartsaklis, D., Sadrzadeh, M., and Pulman, S. (2012). A unified sentence space for categorical distributional-compositional semantics: Theory and experiments. In Proceedings of 24th International Conference on Computational Linguistics (COLING 2012): Posters, pages 549–558, Mumbai, India. The COLING 2012 Organizing Committee.
12. Kintsch, W. (2001). Predication. Cognitive Science, 25(2):173–202.
13. Manandhar, S., Klapaftis, I., Dligach, D., and Pradhan, S. (2010). Semeval-2010 task 14: Word sense induction & disambiguation. In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 63–68. Association for Computational Linguistics.
14. Milligan, G. and Cooper, M. (1985). An Examination of Procedures for Determining the Number of Clusters in a Data Set. Psychometrika, 50(2):159–179.
15. Mitchell, J. and Lapata, M. (2008). Vector-based Models of Semantic Composition. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pages 236–244.
16. Mitchell, J. and Lapata, M. (2010). Composition in distributional models of semantics. Cognitive Science, 34(8):1388–1439.
17. Müllner, D. (2013). fastcluster: Fast Hierarchical Clustering Routines for R and Python. Journal of Statistical Software, 9(53):1–18.
18. Pickering, M. and Frisson, S. (2001). Processing ambiguous verbs: Evidence from eye movements. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(2):556.
19. Pulman, S. (2013). Combining Compositional and Distributional Models of Semantics. In Heunen, C., Sadrzadeh, M., and Grefenstette, E., editors, Quantum Physics and Linguistics: A Compositional, Diagrammatic Discourse. Oxford University Press.
20. Reddy, S., Klapaftis, I., McCarthy, D., and Manandhar, S. (2011). Dynamic and static prototype vectors for semantic composition. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 705–713.
21. Rosenberg, A. and Hirschberg, J. (2007). V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 410–420.
22. Savova, G., Therneau, T., and Chute, C. (2006). Cluster Stopping Rules for Word Sense Discrimination. In Proceedings of the workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together, pages 9–16.
23. Schütze, H. (1998). Automatic Word Sense Discrimination. Computational Linguistics, 24:97–123.
24. Socher, R., Huang, E., Pennington, J., Ng, A., and Manning, C. (2011). Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. Advances in Neural Information Processing Systems, 24.
25. Socher, R., Huval, B., Manning, C., and A., N. (2012). Semantic compositionality through recursive matrix-vector spaces. In Conference on Empirical Methods in Natural Language Processing 2012.
26. Socher, R., Manning, C., and Ng, A. (2010). Learning Continuous Pphrase Representations and Syntactic Parsing with recursive neural networks. In Proceedings of the NIPS-2010 Deep Learning and Unsupervised Feature Learning Workshop.
27. Vendramin, L., Campello, R., and Hruschka, E. (2009). On the Comparison of Relative Clustering Validity Criteria. In Proceedings of the SIAM International Conference on Data Mining, SIAM, pages 733–744.
28. Yao, X. and Van Durme, B. (2011). Nonparametric bayesian word sense induction. ACL HLT 2011, page 10.