T. Kudo and Y. Matsumoto, Chunking with Support Vector Machines, Proceedings of the Second Meeting of the North American Chapter of the Association for Computational Linguistics on Language Technologies, ser. NAACL '01, pp.1-8, 2001.
DOI : 10.3115/1073336.1073361
URL : http://acl.ldc.upenn.edu/N/N01/N01-1025.pdf

Y. He and S. Young, Semantic processing using the Hidden Vector State model, Computer Speech & Language, vol.19, issue.1, pp.85-106, 2005.
DOI : 10.1016/j.csl.2004.03.001
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.8609

S. Hahn, M. Dinarelli, C. Raymond, F. Lefèvre, P. Lehnen et al., Comparing Stochastic Approaches to Spoken Language Understanding in Multiple Languages, IEEE Transactions on Audio, Speech, and Language Processing, vol.19, issue.6, pp.1569-1583, 2011.
DOI : 10.1109/TASL.2010.2093520
URL : https://hal.archives-ouvertes.fr/hal-00746965

G. Mesnil, X. He, L. Deng, and Y. Bengio, Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding, INTERSPEECH 2013 14th Annual Conference of the International Speech Communication Association, pp.3771-3775, 2013.

G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L. Deng et al., Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.23, issue.3, pp.530-539, 2015.
DOI : 10.1109/TASLP.2014.2383614

V. Vukotic, C. Raymond, and G. Gravier, Is it time to switch to word embedding and recurrent neural networks for spoken language understanding, InterSpeech, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01196915

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space, International Conference on Learning Representations, 2013.

R. Lebret and R. Collobert, Word Embeddings through Hellinger PCA, Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics
DOI : 10.3115/v1/E14-1051
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.412.7939

K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig et al., Spoken language understanding using long short-term memory neural networks, 2014 IEEE Spoken Language Technology Workshop (SLT), pp.189-194, 2014.
DOI : 10.1109/SLT.2014.7078572

D. A. Dahl, M. Bates, M. Brown, W. Fisher, K. Hunicke-smith et al., Expanding the scope of the ATIS task, Proceedings of the workshop on Human Language Technology , HLT '94, pp.43-48, 1994.
DOI : 10.3115/1075812.1075823

C. Raymond and G. Riccardi, Generative and Discriminative Algorithms for Spoken Language Understanding, InterSpeech, pp.1605-1608, 2007.

S. Hahn, P. Lehnen, C. Raymond, and H. Ney, A comparison of various methods for concept tagging for spoken language understanding, Proceedings of the Language Resources and Evaluation Conference, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01321122

H. Bonneau-maynard, S. Rosset, C. Ayache, A. Kuhn, and D. Mostefa, Semantic Annotation of the French Media Dialog Corpus, InterSpeech, 2005.

X. Ma and E. Hovy, End-to-end sequence labeling via bidirectional lstm-cnns-crf, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016.
DOI : 10.18653/v1/p16-1101
URL : http://arxiv.org/abs/1603.01354

G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, Neural Architectures for Named Entity Recognition, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.
DOI : 10.18653/v1/N16-1030
URL : http://arxiv.org/abs/1603.01360

J. L. Elman, Finding Structure in Time, Cognitive Science, vol.49, issue.2, pp.179-211, 1990.
DOI : 10.1007/BF00308682

M. I. Jordan, Serial order: A parallel, distributed processing approach Practical recommendations for gradient-based training of deep architectures, Advances in Connectionist Theory, 1206.

M. Dinarelli and I. Tellier, New recurrent neural network variants for sequence labeling, Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01489955

D. Bonadiman, A. Severyn, and A. Moschitti, Recurrent context window networks for italian named entity recognizer, Italian Journal of Computational Linguistics, vol.2, 2016.

M. Schuster and K. Paliwal, Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, vol.45, issue.11, pp.2673-2681, 1997.
DOI : 10.1109/78.650093
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.9441

A. Laurent, N. Camelin, and C. Raymond, Boosting bonsai trees for efficient features combination : application to speaker role identification, InterSpeech, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01025171

K. Yao, G. Zweig, M. Hwang, Y. Shi, and D. Yu, Recurrent Neural Networks for Language Understanding, InterSpeech. Interspeech, 2013.

G. Tur, D. Hakkani-tur, and L. Heck, What is left to be understood in ATIS?, 2010 IEEE Spoken Language Technology Workshop, pp.19-24, 2010.
DOI : 10.1109/SLT.2010.5700816

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014.