E. Agirre, E. Alfonseca, K. Hall, J. Kravalova, M. Pas¸capas¸ca et al., A study on similarity and relatedness using distributional and WordNet-based approaches, Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics on, NAACL '09, 2009.
DOI : 10.3115/1620754.1620758

S. Baker, R. Reichart, and A. Korhonen, An Unsupervised Model for Instance Level Subcategorization Acquisition, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.278-289, 2014.
DOI : 10.3115/v1/D14-1034

P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching word vectors with subword information, 2016.

E. Bruni, G. Boleda, M. Baroni, and N. Tran, Distributional semantics in technicolor, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, pp.136-145, 2012.

A. Ettinger and T. Linzen, Evaluating vector space models using human semantic priming results, Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, pp.72-77, 2016.
DOI : 10.18653/v1/W16-2513

M. Faruqui and C. Dyer, Improving Vector Space Word Representations Using Multilingual Correlation, Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, 2014.
DOI : 10.3115/v1/E14-1049

M. Faruqui, Y. Tsvetkov, P. Rastogi, and C. Dyer, Problems with evaluation of word embeddings using word similarity tasks. arXiv preprint, 2016.

L. Finkelstein, E. Gabrilovich, Y. Matias, E. Rivlin, Z. Solan et al., Placing search in context, Proceedings of the tenth international conference on World Wide Web , WWW '01, pp.406-414, 2001.
DOI : 10.1145/371920.372094

D. Gerz, I. Vuli´cvuli´c, F. Hill, R. Reichart, and A. Korhonen, SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.
DOI : 10.18653/v1/D16-1235

S. Ghannay, Y. Benoit-favre, N. Esteve, and . Camelin, Word embedding evaluation and combination, of the Language Resources and Evaluation Conference Portoroz (Slovenia), pp.23-28, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01433185

G. Halawi, G. Dror, E. Gabrilovich, and Y. Koren, Large-scale learning of word relatedness with constraints, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.1406-1414, 2012.
DOI : 10.1145/2339530.2339751

F. Hill, R. Reichart, and A. Korhonen, SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation, Computational Linguistics, vol.41, issue.4, 2016.
DOI : 10.3115/981732.981751
URL : http://doi.org/10.1162/coli_a_00237

G. Hollis and C. Westbury, The principals of meaning: Extracting semantic dimensions from co-occurrence models of semantics, Psychonomic Bulletin & Review, vol.21, issue.6, pp.1744-1756, 2016.
DOI : 10.2307/410199

A. Keith, . Hutchison, A. David, . Balota, H. James et al., The semantic priming project, Behavior Research Methods, vol.45, issue.4, pp.1099-1114, 2013.

A. Keith, . Hutchison, H. James, . Neely, D. Jeffrey et al., With great expectations, can two " wrongs " prime a " right, Journal of Experimental Psychology Learning Memory and Cognition, vol.27, issue.6, pp.1451-1463, 2001.

S. Lai, K. Liu, S. He, and J. Zhao, How to Generate a Good Word Embedding, IEEE Intelligent Systems, vol.31, issue.6, pp.5-14, 2016.
DOI : 10.1109/MIS.2016.45

O. Levy and Y. Goldberg, Dependencybased word embeddings, ACL (2). Citeseer, pp.302-308, 2014.

O. Levy, Y. Goldberg, and I. Ramat-gan, Linguistic Regularities in Sparse and Explicit Word Representations, Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pp.171-180, 2014.
DOI : 10.3115/v1/W14-1618

T. Luong, R. Socher, D. Christopher, and . Manning, Better word representations with recursive neural networks for morphology, CoNLL, pp.104-113, 2013.

P. Timothy and . Mcnamara, Semantic priming: Perspectives from memory and word recognition, 2005.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space. arXiv preprint, 2013.

A. George, . Miller, G. Walter, and . Charles, Contextual correlates of semantic similarity, Language and cognitive processes, vol.6, issue.1, pp.1-28, 1991.

J. Pennington, R. Socher, D. Christopher, and . Manning, Glove: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1532-1543, 2014.
DOI : 10.3115/v1/D14-1162

F. Pereira, S. Gershman, S. Ritter, and M. Botvinick, A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data, Cognitive Neuropsychology, vol.20, issue.3-4, pp.3-4175, 2016.
DOI : 10.3758/BRM.40.1.183