A. Bibliography, N. Abeillé, and . Barrier, Enriching a French treebank, Proceedings of the 4th International Conference on Language Resources and Evaluation, p.32, 2004.

A. Abeillé, L. Clément, and F. Toussenel, Building a treebank for French. Treebanks, pp.165-187, 2003.

D. Agirre and . Martinez, Learning class-to-class selectional preferences, Proceedings of the 2001 workshop on Computational Natural Language Learning , ConLL '01, pp.15-22, 2001.
DOI : 10.3115/1117822.1117825

T. Agirre, D. Baldwin, and . Martinez, Improving parsing and PP attachment performance with sense information, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp.317-325, 2008.

E. Agirre, K. Alfonseca, J. Hall, M. Kravalova, A. Pa¸scapa¸sca 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, pp.19-27, 2009.
DOI : 10.3115/1620754.1620758
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.6126

K. Agirre, K. Bengoetxea, J. Gojenola, and . Nivre, Improving dependency parsing with semantic classes, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp.699-703, 2011.

A. Arun and F. Keller, Lexicalization in crosslinguistic probabilistic parsing, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , ACL '05, pp.306-313, 2005.
DOI : 10.3115/1219840.1219878

. Attardi, Experiments with a multilanguage non-projective dependency parser, Proceedings of the Tenth Conference on Computational Natural Language Learning, CoNLL-X '06, pp.166-170, 2006.
DOI : 10.3115/1596276.1596307

G. Attardi and M. Ciaramita, Tree revision learning for dependency parsing, Proceedings of the Conference of the North American Chapter, pp.388-395, 2007.

F. Attardi, . Dell, and . Orletta, Reverse revision and linear tree combination for dependency parsing, Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers on, NAACL '09, pp.261-264, 2009.
DOI : 10.3115/1620853.1620925
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.6297

M. Atterer and H. Schütze, Prepositional Phrase Attachment without Oracles, Computational Linguistics, vol.19, issue.1, pp.469-476, 2007.
DOI : 10.1162/coli.2006.32.3.341
URL : http://doi.org/10.1162/coli.2007.33.4.469

M. Bacchiani, M. Riley, B. Roark, and R. Sproat, Map adaptation of stochastic grammars. Computer speech & language, pp.41-68, 2006.

J. M. Balfourier, P. Blache, M. L. Guénot, and T. Vanrullen, Comparaison de trois analyseurs symboliques pour une tâche d'annotation syntaxique, Actes de la 12ème conférence sur le traitement automatique des langues naturelles, pp.41-48, 2005.

A. L. Berger, V. J. Pietra, and S. A. Pietra, A maximum entropy approach to natural language processing, Computational Linguistics, vol.22, issue.1, pp.39-71, 1996.

D. Bergsma, R. Lin, and . Goebel, Discriminative learning of selectional preference from unlabeled text, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.59-68, 2008.
DOI : 10.3115/1613715.1613725

M. Bikel, A statistical model for parsing and word-sense disambiguation, Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics -, pp.155-163, 2000.
DOI : 10.3115/1117794.1117814

S. Bird, E. Loper, and E. Klein, Natural Language Processing with Python, p.133, 2009.

J. Blitzer, R. Mcdonald, and F. Pereira, Domain adaptation with structural correspondence learning, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP '06, pp.120-128, 2006.
DOI : 10.3115/1610075.1610094

A. Böhmová, J. Haji?, E. Haji?ová, and B. Hladká, The Prague dependency treebank, Treebanks, pp.103-127, 2003.

B. Bohnet, Very high accuracy and fast dependency parsing is not a contradiction, Proceedings of the 23rd International Conference on Computational Linguistics, pp.89-97, 2010.

B. Bohnet, Comparing advanced graph-based and transition-based dependency parsers, Proceedings of the International Conference on Dependency Linguistics, pp.282-289, 2011.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

D. Bourigault, Upery: un outil d'analyse distributionnellé etendue pour la construction d'ontologiesàontologiesà partir de corpus, Actes de la 9` eme conférence sur le traitement automatique des langues naturelles, pp.75-84, 2002.

J. Bresnan, A. Cueni, T. Nikitina, and H. Baayen, Cognitive foundations of interpretation, Predicting the dative alternation, pp.69-94, 2007.

J. Briscoe and . Carroll, Automatic extraction of subcategorization from corpora, Proceedings of the fifth conference on Applied natural language processing -, pp.356-363, 1997.
DOI : 10.3115/974557.974609

S. Brody and M. Lapata, Good neighbors make good senses, Proceedings of the 22nd International Conference on Computational Linguistics, COLING '08, pp.65-72, 2008.
DOI : 10.3115/1599081.1599090

P. F. Brown, P. V. Desouza, R. L. Mercer, V. J. Pietra, and J. C. Lai, Class-based n-gram models of natural language, Computational Linguistics, vol.18, issue.4, pp.467-479, 1992.

S. Buchholz and E. Marsi, CoNLL-X shared task on multilingual dependency parsing, Proceedings of the Tenth Conference on Computational Natural Language Learning, CoNLL-X '06, pp.149-164, 2006.
DOI : 10.3115/1596276.1596305

R. C. Bunescu, Learning with probabilistic features for improved pipeline models, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.670-679, 2008.
DOI : 10.3115/1613715.1613799

M. Candito and B. Crabbé, Improving generative statistical parsing with semisupervised word clustering, Proceedings of the 11th International Conference on Parsing Technologies, pp.138-141, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00495267

M. Candito and D. Seddah, Le corpus Sequoia : Annotation syntaxique et exploitation pour l'adaptation d'analyseur par pont lexical, Actes de la 19ème conférence sur le traitement automatique des langues naturelles, p.134, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00698938

M. Candito and D. Seddah, Effectively long-distance dependencies in French: annotation and parsing evaluation, The 11th International Workshop on Treebanks and Linguistic Theories, 1924.
URL : https://hal.archives-ouvertes.fr/hal-00769625

M. Candito, B. Crabbé, and P. Denis, Statistical French dependency parsing: Treebank conversion and first results, Proceedings of the 7th International Conference on Language Resources and Evaluation, pp.37-38, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00495196

M. Candito, J. Nivre, P. Denis, and E. H. Anguiano, Benchmarking of statistical dependency parsers for French, Proceedings of the 23rd International Conference on Computational Linguistics, pp.108-116
URL : https://hal.archives-ouvertes.fr/hal-00514815

M. Candito, E. H. Anguiano, and D. Seddah, A word clustering approach to domain adaptation: Effective parsing of biomedical texts, Proceedings of the 12th International Conference on Parsing Technologies, pp.99-135, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00659577

X. Carreras, Experiments with a higher-order projective dependency parser, Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL, pp.957-961, 2007.

J. Carroll, G. Minnen, and T. Briscoe, Can subcategorisation probabilities help a statistical parser, Proceedings of the 6th ACL/SIGDAT Workshop On Very Large Corpora, pp.118-126, 1998.

D. Cer, M. C. De-marneffe, D. Jurafsky, and C. D. Manning, Parsing to Stanford dependencies: Trade-offs between speed and accuracy, Proceedings of LREC, pp.1628-1632, 2010.

C. C. Chang and C. J. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.27-44, 2011.
DOI : 10.1145/1961189.1961199

E. Charniak, Statistical parsing with a context-free grammar and word statistics, Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp.598-603, 1997.

E. Charniak, Immediate-head parsing for language models, Proceedings of the 39th Annual Meeting on Association for Computational Linguistics , ACL '01, pp.124-131, 2001.
DOI : 10.3115/1073012.1073029
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.5888

E. Charniak and M. Johnson, -best parsing and MaxEnt discriminative reranking, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , ACL '05, pp.173-180, 2005.
DOI : 10.3115/1219840.1219862
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.3530

. Chomsky, Syntactic Structures, p.18, 1957.
DOI : 10.1515/9783110218329

G. Chrupala, Efficient induction of probabilistic word classes with LDA, Proceedings of 5th International Joint Conference on Natural Language Processing, pp.363-372, 2011.

J. Cocke and J. T. Schwartz, Programming languages and their compilers: Preliminary notes, Courant Institute of Mathematical Sciences, p.28, 1969.

M. Collins, Discriminative training methods for hidden Markov models, Proceedings of the ACL-02 conference on Empirical methods in natural language processing , EMNLP '02, pp.1-8, 2002.
DOI : 10.3115/1118693.1118694

M. Collins, Head-Driven Statistical Models for Natural Language Parsing, Computational Linguistics, vol.18, issue.1, pp.589-637, 2003.
DOI : 10.1109/18.87000

M. Collins and T. Koo, Discriminative Reranking for Natural Language Parsing, Computational Linguistics, vol.19, issue.2, pp.25-70, 2005.
DOI : 10.1145/1968.1972

M. Collins, L. Ramshaw, J. Haji?, and C. Tillmann, A statistical parser for Czech, Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics -, pp.505-512, 1999.
DOI : 10.3115/1034678.1034754

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

M. A. Covington, A fundamental algorithm for dependency parsing, Proceedings of the 39th annual ACM southeast conference, pp.95-102, 2001.

B. Crabbé and M. Candito, Expériences d'analyse syntaxique statistique du français, Actes de la 15ème conférence annuelle sur le Traitement Automatique des Langues, pp.32-75, 2008.

K. Crammer and Y. Singer, Ultraconservative Online Algorithms for Multiclass Problems, The Journal of Machine Learning Research, vol.3, pp.951-991, 2003.
DOI : 10.1007/3-540-44581-1_7

K. Crammer, O. Dekel, J. Keshet, S. Shalev-shwartz, and Y. Singer, Online passiveaggressive algorithms, The Journal of Machine Learning Research, vol.7, pp.551-585, 2006.

J. R. Curran, From distributional to semantic similarity, pp.122-123, 2004.

I. Dagan, S. Marcus, and S. Markovitch, Contextual word similarity and estimation from sparse data, Proceedings of the 31st annual meeting on Association for Computational Linguistics, pp.164-171, 1993.

I. Dagan, F. Pereira, and L. Lee, Similarity-based estimation of word cooccurrence probabilities, Proceedings of the 32nd annual meeting on Association for Computational Linguistics -, pp.272-278, 1994.
DOI : 10.3115/981732.981770

I. Dagan, L. Lee, and F. Pereira, Similarity-based methods for word sense disambiguation, Proceedings of the 35th annual meeting of the Association for Computational Linguistics, pp.56-63, 1997.

I. Daumé, A. Kumar, and A. Saha, Frustratingly easy semi-supervised domain adaptation, Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing, pp.53-59, 2010.

P. Denis and B. Sagot, Coupling an annotated corpus and a morphosyntactic lexicon for state-of-the-art POS tagging with less human effort, Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, p.76, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00514366

D. Dowty, Thematic proto-roles and argument selection. Language, pp.547-619, 1991.

J. Earley, An efficient context-free parsing algorithm, Communications of the ACM, vol.13, issue.2, pp.94-102, 1970.
DOI : 10.1145/362007.362035
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.1808

J. M. Eisner, Three new probabilistic models for dependency parsing, Proceedings of the 16th conference on Computational linguistics -, pp.340-345, 1996.
DOI : 10.3115/992628.992688

K. Erk, A simple, similarity-based model for selectional preferences, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pp.216-223, 2007.

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. Pado, Exemplar-based models for word meaning in context, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.92-97, 2010.

K. Van-den-eynde and P. Mertens, La valence: l'approche pronominale et son application au lexique verbal, Journal of French Language Studies, vol.13, issue.1, pp.63-104, 2003.
DOI : 10.1017/S0959269503001005

R. E. Fan, K. W. Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin, LIBLINEAR: A library for large linear classification, The Journal of Machine Learning Research, pp.1871-1874, 2008.

O. Ferret, Discovering word senses from a network of lexical cooccurrences, Proceedings of the 20th international conference on Computational Linguistics , COLING '04, pp.1326-1332, 2004.
DOI : 10.3115/1220355.1220549

O. Ferret, Testing semantic similarity measures for extracting synonyms from a corpus, Proceedings of the 7th International Conference on Language Resources and Evaluation, p.119, 2010.

H. Gaifman, Dependency systems and phrase-structure systems, Information and Control, vol.8, issue.3, pp.304-337, 1965.
DOI : 10.1016/S0019-9958(65)90232-9

P. Gamallo, A. Agustini, and G. P. Lopes, Clustering Syntactic Positions with Similar Semantic Requirements, Computational Linguistics, vol.24, issue.1, pp.107-146, 2005.
DOI : 10.1023/A:1011493212459

Y. Goldberg and M. Elhadad, An efficient algorithm for easy-first non-directional dependency parsing, Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp.742-750, 2010.

. Grefenstette, Explorations in automatic thesaurus discovery, p.118, 1994.
DOI : 10.1007/978-1-4615-2710-7

M. Gross, Constructing lexicon-grammars, Computational approaches to the lexicon, pp.213-263, 1994.
URL : https://hal.archives-ouvertes.fr/hal-00621532

K. Hall and V. Novák, Corrective modeling for non-projective dependency parsing, Proceedings of the Ninth International Workshop on Parsing Technology, Parsing '05, pp.42-52, 1993.
DOI : 10.3115/1654494.1654499

K. Hara, M. Shimbo, H. Okuma, and Y. Matsumoto, Coordinate structure analysis with global structural constraints and alignment-based local features, Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2, ACL-IJCNLP '09, pp.967-975, 2009.
DOI : 10.3115/1690219.1690282

R. Hastie, J. Tibshirani, and . Friedman, The elements of statistical learning, p.125, 2008.

D. G. Hays, Dependency Theory: A Formalism and Some Observations, Language, vol.40, issue.4, pp.511-525, 1964.
DOI : 10.2307/411934

H. Anguiano and M. Candito, Parse correction with specialized models for difficult attachment types, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, p.88, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00602083

H. Anguiano and M. Candito, Probabilistic lexical generalization for French dependency parsing, Proceedings of the ACL Workshop SPSemMRL, p.134, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00699675

H. Anguiano and P. Denis, FreDist: Automatic construction of distributional thesauri for French, Actes de la 18ème conférence sur le traitement automatique des langues naturelles, pp.119-124, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00602004

. Hindle, Noun classification from predicate-argument structures, Proceedings of the 28th annual meeting on Association for Computational Linguistics -, pp.268-275, 1990.
DOI : 10.3115/981823.981857

M. Hindle and . Rooth, Structural ambiguity and lexical relations, Computational linguistics, vol.19, issue.1, pp.103-120, 1993.
DOI : 10.3115/981344.981374
URL : http://acl.ldc.upenn.edu/J/J93/J93-1005.pdf

. Hogan, Coordinate noun phrase disambiguation in a generative parsing model, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pp.680-91, 2007.

W. Hsu and C. J. Lin, A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, vol.13, issue.2, pp.415-425, 2002.

J. J. Jiang and D. W. Conrath, Semantic similarity based on corpus statistics and lexical taxonomy, International Conference on Research in Computational Linguistics, p.133, 1997.

. Joachims, Making large scale SVM learning practical Advances in Kernel Methods -Support Vector Learning, p.103, 1999.

T. Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.133-142, 2002.
DOI : 10.1145/775047.775067
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.3161

R. Johansson and P. Nugues, Extended constituent-to-dependency conversion for english, Proceedings of the 16th Nordic Conference on Computational Linguistics, pp.105-112, 2007.

M. Johnson, PCFG models of linguistic tree representations, Computational Linguistics, vol.24, issue.4, pp.613-632, 1998.

. Kahane, Bubble trees and syntactic representations

. Kahane, Grammaires de dépendance formelles et théorie Sens-Texte, Actes de la 8` eme conférence sur le traitement automatique des langues naturelles, pp.17-76, 2001.

M. Kaplan and J. Bresnan, Lexical-functional grammar: A formal system for grammatical representation. Formal Issues in Lexical-Functional Grammar, pp.29-130, 1982.

. Kasami, An efficient recognition and syntax-analysis algorithm for context-free languages, p.28, 1965.

R. S. Kayne, French syntax: The transformational cycle, p.30, 1975.

J. Kazama, S. De-saeger, K. Kuroda, M. Murata, and K. Torisawa, A Bayesian method for robust estimation of distributional similarities, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.247-256, 2010.

D. Klein and C. D. Manning, Accurate unlexicalized parsing, Proceedings of the 41st Annual Meeting on Association for Computational Linguistics , ACL '03, pp.423-430, 2003.
DOI : 10.3115/1075096.1075150
URL : http://acl.ldc.upenn.edu/acl2003/main/pdf/Klein.pdf

T. Koo and M. Collins, Efficient third-order dependency parsers, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.1-11, 2010.

T. Koo, X. Carreras, and M. Collins, Simple semi-supervised dependency parsing, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp.595-603, 2008.

. Kübler, The PaGe 2008 shared task on parsing German, Proceedings of the Workshop on Parsing German, PaGe '08, pp.55-63, 2008.
DOI : 10.3115/1621401.1621409

S. Kübler, R. Mcdonald, and J. Nivre, Dependency Parsing, Synthesis Lectures on Human Language Technologies, vol.2, issue.1, pp.1-127, 2009.
DOI : 10.2200/S00169ED1V01Y200901HLT002

L. Roux, B. Favre, G. Mirroshandel, and A. Nasr, Modèles génératif et discriminant en analyse syntaxique : Expériences sur le corpus arboré de Paris 7, Actes de la 18ème conférence sur le traitement automatique des langues naturelles, pp.371-382, 2011.

. Lin, Automatic retrieval and clustering of similar words, Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, pp.768-774, 1998.

X. Lin, Y. Fan, M. Zhang, X. Wu, and H. Chi, Refining grammars for parsing with hierarchical semantic knowledge, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 3, EMNLP '09, pp.1298-1307, 2009.
DOI : 10.3115/1699648.1699676

M. Magerman, Natural language parsing as statistical pattern recognition, p.29, 1994.

D. M. Magerman, Statistical decision-tree models for parsing, Proceedings of the 33rd annual meeting on Association for Computational Linguistics -, pp.276-283, 1995.
DOI : 10.3115/981658.981695

B. C. Manning and H. Schütze, Foundations of Statistical Natural Language Processing, p.118, 1999.

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.

D. Mccarthy and J. Carroll, Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences, Computational Linguistics, vol.24, issue.2, pp.639-654, 2003.
DOI : 10.1162/089120101317066104

R. Mccarthy, J. Koeling, J. Weeds, and . Carroll, Finding predominant word senses in untagged text, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics , ACL '04, pp.279-286, 2004.
DOI : 10.3115/1218955.1218991

D. Mcclosky and E. Charniak, Self-training for biomedical parsing, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies Short Papers, HLT '08, pp.101-104, 2008.
DOI : 10.3115/1557690.1557717

D. Mcclosky, E. Charniak, and M. Johnson, Effective self-training for parsing, Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -, pp.152-159, 2006.
DOI : 10.3115/1220835.1220855

R. Mcdonald and J. Nivre, Characterizing the errors of data-driven dependency parsing models, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.122-131, 2007.

R. Mcdonald and F. Pereira, Online learning of approximate dependency parsing algorithms, Proceedings of the 11th Conference of the European Chapter, pp.81-88, 2006.

K. Mcdonald, F. Crammer, and . Pereira, Online large-margin training of dependency parsers, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , ACL '05, pp.91-98, 2005.
DOI : 10.3115/1219840.1219852

K. Mcdonald, F. Lerman, and . Pereira, Multilingual dependency analysis with a two-stage discriminative parser, Proceedings of the Tenth Conference on Computational Natural Language Learning, CoNLL-X '06, pp.216-220, 2006.
DOI : 10.3115/1596276.1596317

I. Mel?uk, Dependency Syntax: Theory and Practice, p.28, 1988.

P. Mertens, Restrictions de sélection et réalisations syntagmatiques dans Dicovalence . Conversion vers un format utilisable en TAL, Actes de la 17ème conférence sur le traitement automatique des langues naturelles, p.153, 2010.

G. A. Miller and W. G. Charles, Contextual correlates of semantic similarity, Language and Cognitive Processes, vol.8, issue.1, pp.1-28, 1991.
DOI : 10.1016/S0022-5371(64)80011-6

G. A. Miller, C. Leacock, R. Tengi, and R. T. Bunker, A semantic concordance, Proceedings of the workshop on Human Language Technology , HLT '93, pp.303-308, 1993.
DOI : 10.3115/1075671.1075742

S. A. Mirroshandel, A. Nasr, and J. L. Roux, Semi-supervised dependency parsing using lexical affinities, Proceedings of ACL 2012, p.147, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00702486

A. Nasr, Analyse syntaxique probabiliste pour grammaires de dépendances extraites automatiquement. HabilitationàHabilitationà diriger des recherches, p.22, 2004.

J. Nivre, An efficient algorithm for projective dependency parsing, Proceedings of the 8th International Workshop on Parsing Technologies, pp.149-160, 2003.

J. Nivre, Incremental non-projective dependency parsing, Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL HLT), pp.396-403, 2007.

J. Nivre, Algorithms for Deterministic Incremental Dependency Parsing, Computational Linguistics, vol.13, issue.4, pp.513-553, 2008.
DOI : 10.1016/0743-1066(95)00035-I
URL : http://doi.org/10.1162/coli.07-056-r1-07-027

J. Nivre and J. Nilsson, Pseudo-projective dependency parsing, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics , ACL '05, pp.99-106, 2005.
DOI : 10.3115/1219840.1219853
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.2372

J. Nivre, J. Hall, J. Nilsson, G. Eryigit, and S. Marinov, Labeled pseudo-projective dependency parsing with support vector machines, Proceedings of the Tenth Conference on Computational Natural Language Learning, CoNLL-X '06, pp.221-225, 2006.
DOI : 10.3115/1596276.1596318
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.577.6929

J. Nivre, J. Hall, S. Kübler, R. Mcdonald, J. Nilsson et al., The CoNLL 2007 shared task on dependency parsing, Proceedings of the CoNLL Shared Task Session of EMNLP-CoNLL, pp.915-932, 2007.

J. Nivre, J. Hall, J. Nilsson, A. Chanev, G. Eryigit et al., MaltParser: A language-independent system for data-driven dependency parsing, Natural Language Engineering, vol.13, issue.81, pp.95-135, 2007.
DOI : 10.1017/S1351324906004505

D. Séaghdha, Latent variable models of selectional preference, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.435-444, 2010.

M. Olteanu and D. Moldovan, PP-attachment disambiguation using large context, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing , HLT '05, pp.273-280, 2005.
DOI : 10.3115/1220575.1220610

P. Pantel and D. Lin, An unsupervised approach to prepositional phrase attachment using contextually similar words, Proceedings of the 38th Annual Meeting on Association for Computational Linguistics , ACL '00, pp.101-108, 2000.
DOI : 10.3115/1075218.1075232
URL : http://acl.ldc.upenn.edu/P/P00/P00-1014.pdf

P. Pantel, E. Crestan, A. Borkovsky, A. Popescu, and V. Vyas, 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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.5814

N. Pereira, L. Tishby, and . Lee, Distributional clustering of English words, Proceedings of the 31st annual meeting on Association for Computational Linguistics -, pp.183-190, 1993.
DOI : 10.3115/981574.981598

S. Petrov and R. Mcdonald, Overview of the 2012 shared task on parsing the web, Notes of the First Workshop on Syntactic Analysis of Non-Canonical Language, p.50, 2012.

S. Petrov, L. Barrett, R. Thibaux, and D. Klein, Learning accurate, compact, and interpretable tree annotation, Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL , ACL '06, pp.433-440, 2006.
DOI : 10.3115/1220175.1220230
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.6643

S. Petrov, P. C. Chang, M. Ringgaard, and H. Alshawi, Uptraining for accurate deterministic question parsing, Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp.705-713, 2010.

J. Pollard and I. A. Sag, Head-Driven Phrase Structure Grammar, p.21, 1994.

P. Resnik, A class-based approach to lexical discovery, Proceedings of the 30th annual meeting on Association for Computational Linguistics -, pp.327-329, 1992.
DOI : 10.3115/981967.982021

P. Resnik, Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language, Journal of Artificial Intelligence Research, vol.11, issue.95, pp.130-91, 1999.

A. Ritter, O. Mausam, and . Etzioni, A latent dirichlet allocation method for selectional preferences, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp.424-434, 2010.

J. J. Robinson, Dependency structures and transformational rules. Language, pp.259-285, 1970.

. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.386-427, 1958.
DOI : 10.1037/h0042519

. Sagae, Self-training without reranking for parser domain adaptation and its impact on semantic role labeling, Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing, pp.37-44, 2010.

K. Sagae and A. Gordon, Clustering words by syntactic similarity improves dependency parsing of predicate-argument structures, Proceedings of the 11th International Conference on Parsing Technologies, IWPT '09, pp.192-201, 2009.
DOI : 10.3115/1697236.1697273

B. Sagot, The Lefff, a freely available, accurate and large-coverage lexicon for French, Proceedings of the 7th International Conference on Language Resources and Evaluation, pp.2010-53

B. Sagot and D. Fi?er, Building a free French wordnet from multilingual resources, Proceedings of Workshop on OntoLex, pp.14-19, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00614708

P. Sgall, E. Hajicová, and J. Panevová, The meaning of the sentence in its semantic and pragmatic aspects, 1921.

M. Shimbo and K. Hara, A discriminative learning model for coordinate conjunctions, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.610-619, 2007.

A. Sigogne and M. Constant, Using subcategorization frames to improve French probabilistic parsing, Proceedings of the 11th Conference on Natural Language Processing (KONVENS'12), p.120, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00790629

A. Sigogne, M. Constant, and E. Laporte, French parsing enhanced with a word clustering method based on a syntactic lexicon, Proceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages, pp.22-27, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00722750

F. Smadja, Retrieving collocations from text: Xtract, Computational linguistics, vol.19, issue.1, pp.143-177, 1993.

M. Steedman, M. Osborne, A. Sarkar, S. Clark, R. Hwa et al., Bootstrapping statistical parsers from small datasets, Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics , EACL '03, pp.331-338, 2003.
DOI : 10.3115/1067807.1067851

. Tesniere, Eléments de syntaxe structurale, p.22, 1959.

J. Tiedemann, News from OPUS ??? A collection of multilingual parallel corpora with tools and interfaces, Recent Advances in Natural Language Processing, pp.237-248, 2009.
DOI : 10.1075/cilt.309.19tie

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.

L. Van-der-plas and G. Bouma, Syntactic contexts for finding semantically related words, Computational linguistics in the Netherlands, p.119, 2004.

G. Van-noord, Using self-trained bilexical preferences to improve disambiguation accuracy, Proceedings of the 10th International Conference on Parsing Technologies, IWPT '07, pp.1-10, 2007.
DOI : 10.3115/1621410.1621411

D. Xiong, S. Li, Q. Liu, S. Lin, and Y. Qian, Parsing the Penn Chinese Treebank with Semantic Knowledge, Proceedings of the International Joint Conference on Natural Language Processing, pp.70-81, 2005.
DOI : 10.1007/11562214_7

H. Yamada and Y. Matsumoto, Statistical dependency analysis with support vector machines, Proceedings of the 8th International Workshop on Parsing Technologies, pp.195-206, 2003.

D. H. Younger, Recognition and parsing of context-free languages in time n3, Information and Control, vol.10, issue.2, pp.189-208, 1967.
DOI : 10.1016/S0019-9958(67)80007-X

Y. Zhang and S. Clark, A tale of two parsers, Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pp.562-571, 2008.
DOI : 10.3115/1613715.1613784

Y. Zhang and J. Nivre, Transition-based parsing with rich non-local features, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp.188-193, 2011.

G. Zhou, J. Zhao, K. Liu, and L. Cai, Exploiting web-derived selectional preference to improve statistical dependency parsing, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp.1556-1565, 2011.