. Bannour, Au d i b e rt, 2011.

. Baroni, Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.238-247, 2014.
DOI : 10.3115/v1/P14-1023

. Baroni, M. Lenci, and A. Et-l-e-n-c-i, Distributional Memory: A General Framework for Corpus-Based Semantics, Computational Linguistics, vol.37, issue.1, pp.673-721, 2010.
DOI : 10.1162/coli.08-032-R1-06-96

. Ai-ku, Using substitute vectors and co-occurrence modeling for word sense induction and disambiguation, Proceedings of SemEval -2013, pp.300-306

. Bengio, Neural Probabilistic Language Models, J. Mach. Learn. Res, vol.3, pp.1137-1155, 2003.
DOI : 10.1007/3-540-33486-6_6

URL : https://hal.archives-ouvertes.fr/hal-01434258

. Blei, Latent dirichlet allocation, J. Mach. Learn. Res, vol.3, pp.993-1022, 2003.

. Bodenreider, Unsupervised, corpus-based method for extending a biomedical terminology, Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain -, pp.2-53, 2002.
DOI : 10.3115/1118149.1118157

. Broda, Rankbased transformation in measuring semantic relatedness, G ao, Y. et J a p kow i c z, N., éditeurs : Canadian Conference on AI, pp.187-190, 2009.

. Buckley, C. Voorhees-y, and E. Vo-o-r-h-e-e-s, Retrieval system evaluation, Vo o r h e e s, E. et H a r m a n, D., éditeurs : TREC : Experiment and Evaluation in Information Retrieval, 2005.

. Budanitsky, A. Hirst-y, and G. Et-h-i-r-s-t, Evaluating WordNet-based Measures of Lexical Semantic Relatedness, Computational Linguistics, vol.17, issue.1, pp.13-47, 2006.
DOI : 10.1016/S0022-5371(79)90604-2

. Bullinaria, J. Levy-r-i-a, and J. Et-l-e-v-y, Extracting semantic representations from word co-occurrence statistics: A computational study, Behavior Research Methods, vol.35, issue.6, pp.510-526, 2007.
DOI : 10.3758/BF03195494

. Bullinaria, J. A. Levy-r-i-a, and J. P. Et-l-e-v-y, Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD, Behavior Research Methods, vol.43, issue.3, pp.890-907, 2012.
DOI : 10.3758/s13428-010-0042-z

W. L. Buntine-et-jakulin-e and A. Et-j-a-k-u-l-i-n, Discrete component analysis, SLSFS, pp.1-33, 2005.

C. and M. S. Hamon, Discovering word senses from text using random indexing A step towards the detection of semantic variants of terms in technical documents, Proceedings of the 9th International International Conference on Computational Linguistics (COLING-ACL'98), pp.498-504, 1998.

A. Henestroza, E. Denis-o, and P. Et-d-e-n-i-s, Fredist : Automatic construction of distributional thesauri for French, Actes de la 18ème conférence TALN, pp.119-124, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00602004

C. M-i-n, Variation terminologique : Reconnaissance et acquisition automatique de termes et de leurs variantes en corpus, 1997.

C. Ac-q-u-e-m-i-n, Spotting and discovering terms through natural language processing, 2001.

W. P. Jones-et-furnas-s and G. W. Et-f-u-r-n-a-s, Pictures of relevance: A geometric analysis of similarity measures, Journal of the American Society for Information Science, vol.38, issue.6, pp.420-442, 1987.
DOI : 10.1002/(SICI)1097-4571(198711)38:6<420::AID-ASI3>3.0.CO;2-S

. Kanerva, K r i s t o f e r s s o n, J. et H o l s t, Random indexing of text samples for latent semantic analysis, éditeurs : Proceedings of the 22nd Annual Conference of the Cognitive Science Society, pp.103-106, 2000.

K. Et-sahlgren-r-e-n, J. Et-s-a-h-l-g-r-e-n, and M. , From words to understanding, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics Foundations of Real-World Intelligence, pp.294-308, 2001.

C. Kiela and S. , A Systematic Study of Semantic Vector Space Model Parameters, Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC), pp.21-30, 2014.
DOI : 10.3115/v1/W14-1503

K. Bibliographie, . Heylen, Y. Speelman-n, D. G. Peirsman, and D. Et-s-p-e-e-l-m-a-n, Modelling word similarity : an evaluation of automatic synonymy extraction algorithms, Language Resources and Evaluation (LREC'08) European Language Resources Association (ELRA), pp.3243-3249, 2008.

. Landauer, T. Dumais-r, and S. Et-d-u-m-a-i-s, A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge., Psychological Review, vol.104, issue.2, p.211, 1997.
DOI : 10.1037/0033-295X.104.2.211

L. Et-seung-]-l-e-e, D. D. Et-s-e-u-n-g, and H. S. , Learning the parts of objects by nonnegative matrix factorization, Nature, vol.401, pp.788-791, 1999.

L. ]-l-e-e, Measures of distributional similarity, Proceedings of ACL-1999, pp.25-32, 1999.

D. ]-l-i-n, Automatic retrieval and clustering of similar words, Proceedings of the 17th international conference on Computational linguistics, pp.768-774, 1998.

D. ]-l-i-n, An information-theoretic definition of similarity, Proceedings of the 15th International Conference on Machine Learning, pp.296-304, 1998.

L. Et-pantel-]-l-i-n, D. Et-pa-n-t-e-l, and P. , Discovery of inference rules for question-answering, Natural Language Engineering, vol.7, issue.4, pp.343-360, 2001.

B. D. Lund, K. Et-b-u-rg-e-s-s, and C. , Producing high-dimensional semantic spaces from lexical co-occurrence, Behavior Research Methods, Instruments, & Computers, vol.19, issue.2, pp.203-208, 1996.
DOI : 10.1007/BF01074363

URL : https://link.springer.com/content/pdf/10.3758%2FBF03204766.pdf

. Lund, B u rg e s s, C. et At c h l e y Semantic and associative priming in high-dimensional semantic space, Proceedings of the 17th Annual Conference of the Cognitive Science Society, pp.660-665, 1995.

. Manning, Introduction to Information Retrieval, 2008.
DOI : 10.1017/CBO9780511809071

. Mikolov, Efficient estimation of word representations in vector space, 2013.

M. Et-lapata-l, J. Pata, and M. , Composition in distributional models of semantics, Cognitive Science, issue.8, pp.341388-1429, 2010.

. Morin, E. Hazem-n, and A. Et-h-a-z-e-m, Looking at Unbalanced Specialized Comparable Corpora for Bilingual Lexicon Extraction, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.1284-1293, 2014.
DOI : 10.3115/v1/P14-1121

URL : https://hal.archives-ouvertes.fr/hal-01097565

E. Bibliographie-[-morin-et-jacquemin-n and C. Ac-q-u-e-m-i-n, Automatic Acquisition and Expansion of Hypernym Links, Computers and the Humanities, vol.38, issue.4, pp.363-396, 2004.

M. Hirst-s, J. Et-h-i-r-s-t, and G. , Non-classical lexical semantic relations, Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics, CLS 04, pp.46-51, 2004.

. Nastase, Semantic Relations Between Nominals, Synthesis Lectures on Human Language Technologies, vol.6, issue.1, 2013.
DOI : 10.2200/S00489ED1V01Y201303HLT019

. Nazarenko, Corpus-based identification and refinement of semantic classes, Proc AMIA Annu Fall Symp, pp.585-594, 1997.

L. Padó, S. Pa-d-Ó, and M. Pata, Dependency-Based Construction of Semantic Space Models, Computational Linguistics, vol.24, issue.1, pp.161-199, 2007.
DOI : 10.1017/S135132490200298X

M. Panchenko, A. Ko, and O. Et-m-o-ro-z-ova, A study of hybrid similarity measures for semantic relation extraction, Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data, pp.10-18, 2012.

. Pantel, Web-scale distributional similarity and entity set expansion, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2, EMNLP '09, pp.938-947, 2009.
DOI : 10.3115/1699571.1699635

URL : http://www.aclweb.org/anthology/D/D09/D09-1098.pdf

. Pedersen, Pat wa r d h a n, S. et M i c h e l i z z i Wordnet : : Similarity : measuring the relatedness of concepts, Demonstration Papers at HLT-NAACL 2004, pp.38-41, 2004.

G. Peirsman, P e i r s m a n, Y. et G e e r a e rt s Predicting strong associations on the basis of corpus data, EACL-2009, pp.648-654, 2009.

. Peirsman, Size matters. tight and loose context definitions in english word space models Generalising and normalising distributional contexts to reduce data sparsity : application to medical corpora, ESSLLI Workshop on Distributional Lexical Semantics CompuTerm 2014 : 4th International Workshop on Computational Terminology, pp.34-41, 2008.

P. Bibliographie, A. Et-hamon-t, and T. Et-h-a-m-o-n, Réduction de la dispersion des données par généralisation des contextes distributionnels : application aux textes de spécialité, Proceedings of TALN 2014, pp.232-243, 2014.

M. Poibeau, Do we still need gold standards for evaluation ?, Proceedings of LREC 2008 European Language Resources Association, pp.1-6, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00321436

. Schölkopf, Kernel principal component analysis, éditeurs : Advances in Kernel Methods, pp.327-352, 1999.
DOI : 10.1007/BFb0020217

. Sun, Ru m s h i s k y, A. et U z u n e r Evaluating temporal relations in clinical text : 2012 i2b2 challenge, JAMIA, vol.20, issue.5, pp.806-813, 2013.

T. Et-panagiotopoulou-s and G. Pa, A generalized vector space model for text retrieval based on semantic relatedness, EACL 2009, pp.70-78, 2009.

. Turney, P. D. Pantel-y, and P. Et-pa-n-t-e-l, From frequency to meaning : Vector space models of semantics, Journal of artificial intelligence research, vol.37, pp.141-188, 2010.

. Bibliographie-[-van-der-plas, G. Bouma, T. P-o-ß, and M. , Syntactic contexts for finding semantically related words R e c k m a n, H. et C r e m e r s, C., éditeurs : Computational Linguistics in the Netherlands, pp.173-186, 2004.

. Van-der-plas and . Tiedemann, 2010] van der P l a s, L. et T i e d e m a n n Finding medical term variations using parallel corpora and distributional similarity, Proceedings of the 6th Workshop on Ontologies and Lexical Resources, pp.28-37, 2010.

S. , E. Rg-a-r-i-t-i-s, and K. G. , Analysis of recommender systems' algorithms, The 6th Hellenic European Conference on Computer Mathematics & its Applications (HERCMA), pp.1-14, 2003.

S. , J. Et-w-e-i-r, and D. , Co-occurrence retrieval : A flexible framework for lexical distributional similarity, Computational Linguistics, issue.4, pp.31439-475, 2005.

. Weeds, Characterising measures of lexical distributional similarity, Proceedings of the 20th international conference on Computational Linguistics , COLING '04, pp.1015-1022, 2004.
DOI : 10.3115/1220355.1220501

. Widdows, D. Ferraro-s, and K. Et-f-e-r-r-a-ro, Semantic vectors : a scalable open source package and online technology management application, Proceedings of the Sixth International Conference on Language Resources and Evaluation) European Language Resources Association (ELRA), pp.1183-1190, 2008.

. Wilks, Providing machine tractable dictionary tools, J. E. Journal of Machine Translation, vol.2, pp.750-755, 1990.
DOI : 10.1007/bf00393758

T. Zesch-et-gurevych and I. Et-g-u-r-e-v-y-c-h, Wisdom of crowds versus wisdom of linguists ??? measuring the semantic relatedness of words, Natural Language Engineering, vol.17, issue.01, pp.25-59, 2010.
DOI : 10.3115/1219840.1219887

. Zheng, Dimensionality Reduction with Category Information Fusion and Non-negative Matrix Factorization for Text Categorization, éditeurs : AICI, volume 7004 de Lecture Notes in Computer Science, pp.505-512, 2011.
DOI : 10.1016/j.eswa.2010.08.066

. Zhitomirsky-geffet and . Dagan, Bootstrapping Distributional Feature Vector Quality, Computational Linguistics, vol.19, issue.1, pp.435-461, 2009.
DOI : 10.1162/089120105775299122

URL : http://doi.org/10.1162/coli.08-032-r1-06-96