D. Dinu, G. Lapata, and M. , Measuring distributional similarity in context, Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp.1162-1172, 2010.

K. Erk and S. Padó, A structured vector space model for word meaning in context, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.897-906, 2008.
DOI : 10.3115/1613715.1613831

K. Erk and S. Padó, Paraphrase assessment in structured vector space, Proceedings of the Workshop on Geometrical Models of Natural Language Semantics, GEMS '09, pp.57-65, 2009.
DOI : 10.3115/1705415.1705423

D. Guthrie, B. Allison, W. Liu, L. Guthrie, and Y. Wilks, A closer look at skipgram modelling, Proceedings of the 5th international Conference on Language Resources and Evaluation (LREC-2006), pp.1-4, 2006.

E. Giesbrecht, Towards a matrix-based distributional model of meaning, Proceedings of the NAACL HLT 2010 Student Research Workshop, pp.23-28, 2010.

E. Grefenstette and M. Sadrzadeh, Experimental support for a categorical compositional distributional model of meaning, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp.1394-1404, 2011.

E. Ggrefenstette and M. Sadrzadeh, Experimenting with transitive verbs in a discocat, Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, pp.62-66, 2011.

[. Landauer, T. K. Foltz, P. W. Laham, and D. , An introduction to latent semantic analysis, Discourse processes, pp.259-284, 1998.
DOI : 10.1080/01638539809545030

A. Mnih and G. Hinton, Three new graphical models for statistical language modelling, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.641-648, 2007.
DOI : 10.1145/1273496.1273577

[. Mitchell, J. Lapata, and M. , Vector-based Models of Semantic Composition, ACL, pp.236-244, 2008.

[. Mitchell, J. Lapata, and M. , Composition in Distributional Models of Semantics, Cognitive Science, vol.14, issue.3, pp.1388-1429, 2010.
DOI : 10.1111/j.1551-6709.2010.01106.x

M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini, Building a large annotated corpus of English: The Penn Treebank, Computational linguistics, vol.19, issue.2, pp.313-330, 1993.

B. Partee, Lexical semantics and compositionality. An invitation to cognitive science: Language, pp.311-360, 1995.

[. Pustejovsky and J. , Lexicon, Generative, Computational linguistics, vol.17, issue.4, pp.409-441, 1991.
DOI : 10.1016/B0-08-044854-2/01971-4

R. Socher, D. Chen, C. D. Manning, and A. Ng, Reasoning with neural tensor networks for knowledge base completion, Advances in Neural Information Processing Systems, pp.926-934, 2013.

R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning et al., Recursive deep models for semantic compositionality over a sentiment treebank, Proceedings of the conference on empirical methods in natural language processing (EMNLP), p.1642, 2013.

B. Santorini, Part-of-speech tagging guidelines for the Penn Treebank Project, 1990.

R. Socher, B. Huval, C. D. Manning, and A. Y. Ng, Semantic compositionality through recursive matrix-vector spaces, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.1201-1211, 2012.

P. D. Turney and P. Pantel, From frequency to meaning: Vector space models of semantics, Journal of artificial intelligence research, vol.37, issue.1, pp.141-188, 2010.

S. Thater, G. Dinu, and M. Pinkal, Ranking paraphrases in context, Proceedings of the 2009 Workshop on Applied Textual Inference, TextInfer '09, pp.44-47, 2009.
DOI : 10.3115/1708141.1708149

S. Thater, H. Fürstenau, and M. Pinkal, Contextualizing semantic representations using syntactically enriched vector models, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.948-957, 2010.

P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol, Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1096-1103, 2008.
DOI : 10.1145/1390156.1390294

T. Van-de-cruys, T. Poibeau, and A. Korhonen, A tensor-based factorization model of semantic compositionality, Conference of the North American Chapter of the Association of Computational Linguistics (HTL-NAACL), pp.1142-1151, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00997334

S. M. Weiss and C. A. Kulikowski, Computer systems that learn: classification and prediction methods from statistics, Machine Learning, and Expert Systems, 1991.

D. Yu, L. Deng, and F. Seide, The deep tensor neural network with applications to large vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, vol.21, issue.2, pp.388-396, 2013.