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J. Berstel and C. Reutenauer, Recognizable formal power series on trees, Theoretical Computer Science, vol.18, issue.2, pp.115-148, 1982.
DOI : 10.1016/0304-3975(82)90019-6

URL : https://hal.archives-ouvertes.fr/hal-00619497

J. Geert, F. Bex, T. Neven, K. Schwentick, and . Tuyls, Inference of concise dtds from xml data, Proceedings of 32nd Conference on Very Large databases -VLDB, pp.115-126, 2006.

J. Carme, R. Gilleron, A. Lemay, and J. Niehren, Interactive learning of node selecting tree transducer, Machine Learning, pp.33-67, 2007.
DOI : 10.1007/s10994-006-9613-8

B. Chidlovskii and J. Fuselier, A probabilistic learning method for xml annotation of documents, Proceedings IJCAI, 19th International Joint Conference on Artificial Intelligence, 2005.

S. Clark and J. R. Curran, Parsing the WSJ using CCG and loglinear models, Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp.103-110, 2004.

W. Cohen, M. Hurst, and L. Jensen, Web Document Analysis: Challenges and Opportunities, chapter A Flexible Learning System for Wrapping Tables and Lists in HTML Documents, 2003.

T. Cohn, Scaling conditional random fields for natural language processing, 2007.

T. Cohn and P. Blunsom, Semantic role labelling with tree conditional random fields, Proceedings of the Ninth Conference on Computational Natural Language Learning, CONLL '05, 2005.
DOI : 10.3115/1706543.1706573

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.90.5867

H. Comon, M. Dauchet, R. Gilleron, C. Löding, F. Jacquemard et al., Tree automata techniques and applications. Available online since 1997: http://tata.gforge. inria.fr, 2007.

A. Culota, T. Kristjansson, A. Mccallum, and P. Viola, Corrective feedback and persistent learning for information extraction, Artificial Intelligence, vol.170, issue.14-15, pp.1101-1122, 2006.
DOI : 10.1016/j.artint.2006.08.001

L. Denoyer and P. Gallinari, Report on the xml mining track at inex 2005 and inex 2006, proceedings of INEX 2006, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00173420

L. Denoyer and P. Gallinari, Report on the XML mining track at INEX 2005 and INEX 2006, ACM SIGIR Forum, vol.41, issue.1, pp.79-90, 2007.
DOI : 10.1145/1273221.1273230

URL : https://hal.archives-ouvertes.fr/inria-00173420

A. Doan, Learning to Map between Structured Representations of Data, 2002.

A. Doan and A. Y. Halevy, Semantic integration research in the database community: A brief survey, AI magazine, vol.26, issue.1, pp.83-94, 2005.

A. Doan, P. Domingos, and A. Halevy, Reconciling schemas of disparate data sources: A machine-learning approach, Proceedings of the ACM SIGMOD Conference, pp.509-520, 2001.

Z. Esik and W. Kuich, Formal tree series, Journal of Automata, Languages and Combinatorics, vol.8, pp.219-285, 2003.

J. R. Finkel, A. Kleeman, and C. D. Manning, Efficient, feature-based, conditional random field parsing, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp.959-967, 2008.

P. Gallinari, G. Wisniewski, L. Denoyer, and F. Maes, Stochastic models for document restructuration, Proceedings of ECML Workshop On Relational Machine Learning, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01357589

G. Gottlob, C. Koch, R. Baumgartner, M. Herzog, and S. Flesca, The Lixto data extraction project -back and forth between theory and practice, 23rd ACM SIGPLAN-SIGACT Symposium on Principles of Database Systems, pp.1-12, 2004.

G. Gottlob and C. Koch, Monadic datalog and the expressive power of languages for Web information extraction, Journal of the ACM, vol.51, issue.1, pp.74-113, 2004.
DOI : 10.1145/962446.962450

C. Hsu and M. Dung, Generating finite-state transducers for semi-structured data extraction from the Web, Information Systems, vol.23, issue.8, pp.521-538, 1998.
DOI : 10.1016/S0306-4379(98)00027-1

I. Michael, Z. Jordan, T. Ghahramani, L. K. Jaakkola, and . Saul, An introduction to variational methods for graphical models, Machine Learning, pp.183-233, 1999.

N. Kushmerick, Wrapper Induction for Information Extraction, 1997.

J. D. Lafferty, A. Mccallum, and F. C. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, Proceedings of the Eighteenth International Conference on Machine Learning (ICML), pp.282-289, 2001.

J. Madhavan, P. Bernstein, K. Chen, A. Halevy, and P. Shenoy, Corpus-Based Schema Matching, 21st International Conference on Data Engineering (ICDE'05), 2003.
DOI : 10.1109/ICDE.2005.39

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.127.2920

A. Mccallum and W. Li, Early results for named entity recognition with conditional random fields, CoNLL'2003: Proceedings of The Seventh Conference on Natural Language Learning, 2003.

A. Mccallum, D. Freitag, and F. Pereira, Maximum entropy Markov models for information extraction and segmentation, Proc. 17th International Conf. on Machine Learning, pp.591-598, 2000.

S. Inria-ion-muslea, C. Minton, and . Knoblock, Active learning with strong and weak views: a case study on wrapper induction, IJCAI 2003, pp.415-420, 2003.

D. Pinto, A. Mccallum, X. Wei, and W. B. Croft, Table extraction using conditional random fields, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval , SIGIR '03, pp.235-242, 2003.
DOI : 10.1145/860435.860479

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.320

S. Raeymaekers, M. Bruynooghe, and J. Van-den-bussche, Learning (k,l)-Contextual Tree Languages for Information Extraction, Proceedings of ECML'2005, pp.305-316, 2005.
DOI : 10.1007/11564096_31

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.493.8914

S. Riezler, T. King, R. Kaplan, R. Crouch, J. Maxwell et al., Parsing the wall street journal using a Lexical-Functional Grammar and discriminative estimation techniques, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics , ACL '02, pp.271-278, 2002.
DOI : 10.3115/1073083.1073129

D. Roth and W. Yih, Integer linear programming inference for conditional random fields, Proceedings of the 22nd international conference on Machine learning , ICML '05, 2005.
DOI : 10.1145/1102351.1102444

S. Sarawagi and W. W. Cohen, Semi-markov conditional random fields for information extraction, Proceedings of NIPS, pp.1185-1192, 2004.

K. Sato and Y. Sakakibara, RNA secondary structural alignment with conditional random fields, Bioinformatics, vol.21, issue.Suppl 2, pp.237-242, 2005.
DOI : 10.1093/bioinformatics/bti1139

URL : http://bioinformatics.oxfordjournals.org/cgi/content/short/21/suppl_2/ii237

P. Senellart, A. Mittal, D. Muschick, R. Gilleron, and M. Tommasi, Automatic wrapper induction from hidden-web sources with domain knowledge, Proceeding of the 10th ACM workshop on Web information and data management, WIDM '08, 2008.
DOI : 10.1145/1458502.1458505

URL : https://hal.archives-ouvertes.fr/inria-00337098

F. Sha and F. Pereira, Shallow parsing with conditional random fields, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology , NAACL '03, pp.213-220, 2003.
DOI : 10.3115/1073445.1073473

URL : http://acl.ldc.upenn.edu/N/N03/N03-1028.pdf

C. Sutton, Conditional probabilistic context-free grammars, 2004.

C. Sutton and A. Mccallum, Introduction to Statistical Relational Learning, chapter An Introduction to Conditional Random Fields for Relational Learning, 2006.

C. Sutton, K. Rohanimanesh, and A. Mccallum, Dynamic conditional random fields, Twenty-first international conference on Machine learning , ICML '04, pp.783-790, 2004.
DOI : 10.1145/1015330.1015422

J. Tang, M. Hong, J. Li, and B. Liang, Tree-Structured Conditional Random Fields for Semantic Annotation, The Semantic Web -ISWC 2006, pp.640-653, 2006.
DOI : 10.1007/11926078_46

P. Viola and M. Narasimhan, Learning to extract information from semistructured text using a discriminative context free grammar, Proceedings of the ACM SIGIR, pp.330-337, 2005.

M. J. Wainwright and M. I. Jordan, Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends?? in Machine Learning, vol.1, issue.1???2, 2003.
DOI : 10.1561/2200000001

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.192.2462

H. Wallach, Efficient training of conditional random fields, 2002.

J. Zhu, Z. Nie, J. Wen, B. Zhang, and W. Ma, 2D Conditional Random Fields for Web information extraction, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.1044-1051, 2005.
DOI : 10.1145/1102351.1102483