M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, p.61, 2015.

D. Ahn, S. F. Adafre, and M. De-rijke, Towards Task-Based Temporal Extraction and Recognition, Annotating, Extracting and Reasoning About Time and Events (Schloss Dagstuhl, p.12, 2005.

J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang, Topic Detection and Tracking Pilot Study: Final Report, Proceedings of the 1998 DARPA Broadcast News Transcription and Understanding fiorkshop, p.20, 1998.

J. Allan, R. Papka, and V. Lavrenko, On-line New Event Detection and Tracking, Proceedings of the 21st ACM/SIGIR International Conference on Research and Developement in Information Retrieval, p.20, 1998.

J. F. Allen, Maintaining Knowledge About Temporal Intervals, Communications of the Association for Computational Machinery 26, vol.11, p.11, 1983.

P. Anick, P. Hong, and N. Xue, Coreference Resolution for Electronic Medical Records, Proceedings of the 2011 i2b2/ffA/Cincinatti fiorkshop on Challenges in Natural Language Processing for Clinical Data, p.121, 2011.

C. Aone and S. W. Bennett, Applying Machine Learning to Anaphora Resolution, Proceedings of the 1995 International Joint Conference on Artiicial Intelligence, vol.81, p.80, 1995.

A. R. Aronson, EEective Mapping of Biomedical Text to the UMLS Metathesaurus: the MetaMap Program, Proceedings of the 2001 AMIA Annual Symposium References, vol.86, pp.17-21, 2001.

M. Asahara, S. Kato, H. Konishi, M. Imada, and K. Maekawa, BCCWJ-TimeBank: Temporal and Event Information Annotation on Japanese Text, International Journal of Computational Linguistics and Chinese Language Processing, vol.19, p.29, 2014.

F. Atefeh and W. Khreich, A Survey of Techniques for Event Detection in Twitter, Computational Intelligence 31.1, p.21, 2015.

E. Bach, The Algebra of Events, Linguistics and Philosophy, vol.9, p.12, 1986.

A. Bagga and B. Baldwin, Algorithms for Scoring Coreference Chains, Proceedings of the 1st International Conference on Language Resources and Evaluation, p.88, 1998.

J. Barwise and J. Perry, Situations and Attitudes, The Journal of Philosophy, vol.78, p.71, 1981.

E. Bengtson and D. Roth, Understanding the Value of Features for Coreference Resolution, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, vol.83, p.81, 2008.

A. L. Bergert, J. Vincent, S. A. Della-pietra, and . Della-pietra, A Maximum Entropy Approach to Natural Language Processing, Computational Linguistics 22.1, vol.82, p.81, 1996.

J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, Algorithms for Hyper-Parameter Optimization, Proceedings of the 2011 Neural Information Processing Systems Conference, vol.57, pp.2546-2554, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00642998

J. Bergstra and Y. Bengio, Random Search for Hyper-Parameter Optimization, Journal of Machine Learning Research, vol.12, p.49, 2012.

J. Bergstra, D. Yamins, and D. Cox, Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures, Proceedings of the 30th International Conference on Machine Learning, vol.57, p.50, 2013.

S. Bethard, Finding Event, Temporal and Causal Structure in Text: A Machine Learning Approach, vol.16, p.13, 2007.

S. Bethard, ClearTK-TimeML: A Minimalist Approach to TempEval, Proceedings of the 7th International fiorkshop on Semantic Evaluation, pp.10-14, 2013.

S. Bethard, L. Derczynski, G. Savova, J. Pustejovsky, and M. Verhagen, SemEval-2015 Task 6: Clinical TempEval, Proceedings of the 9th International fiorkshop on Semantic Evaluation, pp.806-814, 2015.

S. Bethard and J. H. Martin, Identiication of Event Mentions and their Semantic Class, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, p.35, 2006.

S. Bethard, P. Ogren, and L. Becker, ClearTK 2.0: Design Patterns for Machine Learning in UIMA, Proceedings of the 9th International Conference on Language Resources and Evaluation, p.34, 2014.

S. Bethard, G. Savova, W. Chen, L. Derczynski, J. Pustejovsky et al., SemEval-2016 Task 12: Clinical TempEval, Proceedings of the 10th International fiorkshop on Semantic Evaluation, pp.1052-1062, 2016.

S. Bethard, G. Savova, M. Palmer, and J. Pustejovsky, SemEval-2017 Task 12: Clinical TempEval, Proceedings of the 11th International fiorkshop on Semantic Evaluation, vol.119, pp.153-155, 2017.

D. Biber, S. Johansson, G. Leech, S. Conrad, and E. Finegan, Longman Grammar of Spoken and firitten English. Longman (cit, p.11, 1999.

S. Bird and E. Loper, Association for Computational Linguistics (cit, Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, p.45, 2004.

A. Bittar, P. Amsili, P. Denis, and L. Danlos, French TimeBank: An ISO-TimeML Annotated Reference Corpus, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol.29, p.11, 2011.
URL : https://hal.archives-ouvertes.fr/halshs-00751076

A. Björkelund and J. Kuhn, Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol.86, p.85, 2014.

A. Björkelund and P. Nugues, Exploring Lexicalized Features for Coreference Resolution, Proceedings of the 15th Conference on Computational Natural References Language Learning, pp.45-50, 2011.

O. Bodenreider, The Uniied Medical Language System (UMLS): Integrating Biomedical Terminology, Nucleic Acids Research, vol.32, p.45, 2004.

B. Boguraev and R. Kubota-ando, TimeML-compliant Text Analysis for Temporal Reasoning, Proceedings of the 19th International Joint Conference on Artiicial Intelligence, p.36, 2005.

P. Bramsen, P. Deshpande, Y. Lee, and R. Barzilay, Finding Temporal Order in Discharge Summaries, Proceedings of the 2006 AMIA Annual Symposium, p.22, 2006.

P. Bramsen, P. Deshpande, Y. Lee, and R. Barzilay, Inducing Temporal Graphs, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp.189-198, 2006.

S. E. Brennan, M. W. Friedman, and C. J. Pollard, A Centering Approach to Pronouns, Proceedings of the 25th Annual Meeting of the Association for Computational Linguistics, p.79, 1987.

K. Brown, Encyclopedia of Language and Linguistics, p.14, 2005.

P. F. Brown, V. Peter, R. L. Desouza, T. J. Mercer, V. J. Watson et al., Class-Based n-gram Models of Natural Language, Computational Linguistics 18, vol.4, p.36, 1992.

J. Cai and M. Strube, Evaluation Metrics For End-to-End Coreference Resolution Systems, Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p.89, 2010.

L. Campillos, L. Deléger, C. Grouin, T. Hamon, A. Ligozat et al., A French Clinical Corpus with Comprehensive Semantic Annotations: Development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT), Language Resources and Evaluation, vol.52, issue.2, pp.571-601, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01631743

C. Cardie and K. Wagstaf, Association for Computational Linguistics, Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and ffery Large Corpora, vol.83, p.80, 1999.

T. Caselli, V. B. Lenzi, R. Sprugnoli, E. Pianta, and I. Prodanof, Annotating Events, Temporal Expressions and Relations in Italian: the It-Timeml Experience for the Ita-TimeBank, Proceedings of the 5th Linguistic AnReferences notation fiorkshop, p.29, 2011.

N. Chambers, NavyTime: Event and Time Ordering from Raw Text, Proceedings of the 7th International fiorkshop on Semantic Evaluation, p.35, 2013.

N. Chambers and D. Jurafsky, Jointly Combining Implicit Constraints Improves Temporal Ordering, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, p.38, 2008.

A. X. Chang and C. D. Manning, SUTime: A Library for Recognizing and Normalizing Time Expressions, Proceedings of the 8th International Conference on Language Resources and Evaluation, 2012.

, European Language Resources Association (cit, vol.35, p.34

K. Chang, R. Samdani, and D. Roth, A Constrained Latent Variable Model for Coreference Resolution, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, vol.86, p.85, 2013.

K. Chang, R. Samdani, A. Rozovskaya, M. Sammons, and D. Roth, Illinois-Coref: The UI System in the CoNLL-2012 Shared Task, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, vol.83, p.82, 2012.

Y. Chang, H. Dai, J. Wu, J. Chen, R. T. Tsai et al., TEMPTING System: A Hybrid Method of Rule and Machine Learning for Temporal Relation Extraction in Patient Discharge Summaries, i2b2 NLP Challenge on Temporal Relations in Clinical Data), vol.46, p.37, 2012.

E. Charniak, Toward A Model of Children's Story Comprehension, p.79, 1972.

E. Charniak and M. Johnson, Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking, Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, p.45, 2005.

C. Cherry, X. Zhu, J. Martin, and B. De-bruijn, À la Recherche du Temps Perdu: Extracting Temporal Relations from Medical Text in the 2012 i2b2 NLP Challenge, Journal of the American Medical Informatics Association, vol.20, p.36, 2013.

N. A. Chinchor, Overview of MUC-7/MET-2, Proceedings of the 7th Message Understanding Conference, vol.22, p.17, 1998.

H. H. Clark and C. R. Marshall, Deenite Knowledge and Mutual Knowledge, Elements of Discourse Understanding, p.71, 1981.

K. Clark and C. D. Manning, Entity-Centric Coreference Resolution with Model Stacking, Proceedings of the Joint Conference of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, pp.1405-1415, 2015.

K. Clark and C. D. Manning, Deep Reinforcement Learning for Mention-Ranking Coreference Models, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp.2256-2262, 2016.

K. Clark and C. D. Manning, Improving Coreference Resolution by Learning Entity-Level Distributed Representations, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp.643-653, 2016.

A. Cohan, K. Meurer, and N. Goharian, GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives, Proceedings of the 10th International fiorkshop on Semantic Evaluation, vol.37, p.35, 2016.

K. B. Cohen, A. Lanfranchi, M. Choi, M. Bada, W. A. Baumgartner et al., Coreference Annotation and Resolution in the Colorado Richly Annotated Full Text (CRAFT) Corpus of Biomedical Journal Articles, BMC Bioinformatics, vol.18, issue.1, p.77, 2017.

W. W. Cohen, Fast EEective Rule Induction, Proceedings of the 12th International Conference on Machine Learning, p.81, 1995.

M. Collins, Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms, Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, p.85, 2002.

B. Comrie, Aspect: An Introduction to the Study of fferbal Aspect and Related Problems (cit, p.12, 1976.

C. Cortes and V. Vapnik, Support-Vector Networks, Machine Learning 20.3, vol.82, p.81, 1995.

F. Costa and A. Branco, TimeBankPT: A TimeML Annotated Corpus of Portuguese, Proceedings of the 8th International Conference on Language Resources and Evaluation, p.29, 2012.

G. Crichton, S. Pyysalo, B. Chiu, and A. Korhonen, A Neural Network Multi-task Learning Approach to Biomedical Named Entity Recognition, BMC Bioinformatics, vol.18, issue.1, p.110, 2017.

A. Culotta, M. Wick, and A. Mccallum, First-Order Probabilistic Models for Coreference Resolution, Proceedings of the 2010 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.81-88, 2007.

W. Daelemans, A. Van-den, and . Bosch, Memory-Based Language Processing, p.81, 2005.

M. Dalrymple, S. M. Shieber, and F. C. Pereira, Ellipsis and HigherOrder Uniication, Linguistics and Philosophy, vol.14, p.71, 1991.

I. Daumé and H. , Practical Structured Learning Techniques for Natural Language Processing, p.84, 2006.

I. Daumé, H. , and D. Marcu, A Large-Scale Exploration of EEective Global Features for a Joint Entity Detection and Tracking Model, Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing, p.84, 2005.

I. Daumé, H. , and D. Marcu, Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction, Proceedings of the 22nd International Conference on Machine Learning, p.84, 2005.

K. Deemter, R. Van, and . Kibble, On Coreferring: Coreference in MUC and Related Annotation Schemes, Computational Linguistics 26, vol.4, pp.629-637, 2000.

P. Denis and J. Baldridge, Specialized Models and Ranking for Coreference Resolution, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp.81-83, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00514368

P. Denis and J. Baldridge, Global Joint Models for Coreference Resolution and Named Entity Classiication, Procesamiento del Lenguaje Natural, vol.42, p.81, 2009.

P. Denis and P. Muller, Predicting Globally-Coherent Temporal Structures from Texts via Endpoint Inference and Graph Decomposition, Proceedings of the 22nd International Joint Conference on Artiicial Intelligence, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00614765

L. R. Derczynski, Automatically Ordering Events and Times in Text, vol.17, 2017.

D. Dligach, T. Miller, C. Lin, S. Bethard, and G. Savova, Neural Temporal Relation Extraction, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pp.746-751, 2017.

D. Dowty, fiord Meaning and Montague Grammar: The Semantics of fferbs and Times in Generative Semantics and in Montague's PTQ, p.12, 1979.

G. Durrett and D. Klein, Easy Victories and Uphill Battles in Coreference Resolution, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp.1971-1982, 2013.

C. Fellbaum, fiordNet: An Electronic Lexical Database (cit, p.86, 1998.

E. Fernandes, C. Rezende, . Nogueira, R. Santos, and . Milidiú, Latent Structure Perceptron with Feature Induction for Unrestricted Coreference Resolution, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, p.85, 2012.

E. Fernandes, C. Rezende, . Nogueira, R. Santos, and . Milidiú, Latent Trees for Coreference Resolution, Computational Linguistics 40, vol.4, p.85, 2014.

L. Ferro, L. Gerber, I. Mani, B. Sundheim, and G. Wilson, TIDES2005 Standard for the Annotation of Temporal Expressions, vol.18, p.11, 2005.

T. Finley and T. Joachims, Supervised Clustering with Support Vector Machines, Proceedings of the 22nd International Conference on Machine Learning, p.84, 2005.

D. Fisher, S. Soderland, J. Mccarthy, F. Feng, and W. Lehnert, Description of the UMass System as used for MUC-6, Proceedings of the 6th Message Understanding Conference, vol.83, p.80, 1995.

C. Forascu and D. Tuus?, Romanian TimeBank: An Annotated Parallel Corpus for Temporal Information, Proceedings of the 8th International Conference on Language Resources and Evaluation, p.29, 2012.

C. Freksa, Temporal reasoning based on semi-intervals, Artiicial Intelligence 54.1, p.17, 1992.

L. Galescu and N. Blaylock, A Corpus of Clinical Narratives Annotated with Temporal Information, Proceedings of the 2nd ACM/SIGHIT International Health References Informatics Symposium, pp.715-720, 2012.

A. Galton, A Critical Examination of Allen's Theory of Action and Time, Artiicial Intelligence, p.16, 1990.

A. Garnham, Mental Models and the Interpretation of Anaphora, vol.74, p.73, 2001.

F. A. Gers, N. Nicol, J. Schraudolph, and . Schmidhuber, Learning Precise Timing with LSTM Recurrent Networks, Journal of Machine Learning Research, vol.3, p.113, 2002.

R. Grishman and B. M. Sundheim, Message Understanding Conference-6: A Brief History, Proceedings of the 17st International Conference on Computational Linguistics and the 36th Annual Meeting of the Association for Computational Linguistics, p.20, 1996.

B. J. Grosz and C. L. Sidner, Attention, Intentions and the Structure of Discourse, Computational Linguistics, vol.12, p.79, 1986.

B. J. Grosz, S. Weinstein, and A. K. Joshi, Centering: A Framework for Modeling the Local Coherence of Discourse, Computational Linguistics 21, vol.2, p.79, 1995.

C. Grouin, M. Dinarelli, S. Rosset, G. Wisniewski, and P. Zweigenbaum, Coreference Resolution in Clinical Reports. The LIMSI Participation in the i2b2/VA 2011 Challenge, Proceedings of the 2011 i2b2/ffA/Cincinnati fiorkshop on Challenges in Natural Language Processing for Clinical Data, 2011.

C. Grouin, N. Grabar, T. Hamon, S. Rosset, X. Tannier et al., Eventual Situations for Timeline Extraction from, Clinical Reports". In: Journal of the American Medical Informatics Association, vol.20, p.37, 2013.
DOI : 10.1136/amiajnl-2013-001627
URL : https://academic.oup.com/jamia/article-pdf/20/5/820/5874704/20-5-820.pdf

C. Grouin and A. Névéol, De-Identiication of Clinical Notes in French: Towards a Protocol for Reference Corpus Developpement, Journal of Biomedical Informatics, vol.50, pp.151-61, 2014.

C. Hagège and X. Tannier, XRCE-T: XIP Temporal Module for TempEval campaign, Proceedings of the 4th International fiorkshop on Semantic Evaluation, p.36, 2007.

H. Harkema, A. Setzer, R. Gaizauskas, and M. Hepple, Mining and Modelling Temporal Clinical Data, Proceedings of the 4th UK e-Science All Hands Meeting, p.33, 2005.

M. Hepple, A. Setzer, and R. Gaizauskas, USFD: Preliminary Exploration of Features and Classiiers for the TempEval-2007 Task, Proceedings of the 4th, 2007.

, Association for Computational Linguistics, References International fiorkshop on Semantic Evaluation, p.36, 2007.

D. Hinote, C. Ramirez, and P. Chen, A Comparative Study of Coreference Resolution in Clinical Text, Proceedings of the 2011 i2b2/ffA/Cincinnati fiorkshop on Challenges in Natural Language Processing for Clinical Data, p.121, 2011.

L. Hirschman and N. A. Chinchor, MUC-7 Coreference Task Deenition, Proceedings of the 7th Message Understanding Conference, vol.87, 1998.

J. R. Hobbs, Pronoun Resolution. Research rep, p.79, 1976.
DOI : 10.1145/1045283.1045292

J. R. Hobbs, Resolving Pronoun References, Lingua 44, p.79, 1978.
DOI : 10.1016/0024-3841(78)90006-2

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation 9, vol.8, p.56, 1997.

V. Hoste, Optimization Issues in Machine Learning of Coreference Resolution, p.81, 2005.

E. Hovy, M. Marcus, M. Palmer, L. Ramshaw, and R. Weischedel, OntoNotes: The 90% Solution, Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.57-60, 2006.

G. Hripcsak, N. D. Soulakis, L. Li, F. P. Morrison, A. M. Lai et al., Syndromic Surveillance Using Ambulatory Electronic Health Records, Journal of the American Medical Informatics Associtation, vol.16, p.22, 2009.
DOI : 10.1197/jamia.m2922
URL : https://academic.oup.com/jamia/article-pdf/16/3/354/2548951/16-3-354.pdf

Z. Huang, W. Xu, and K. Yu, Bidirectional LSTM-CRF Models for Sequence Tagging, Computing Research Repository, p.56, 2015.

P. Jindal and D. Roth, Using Domain Knowledge and Domain-Inspired Discourse Model for Coreference Resolution in Clinical Narratives, Proceedings of the 2011 i2b2/ffA/Cincinnati fiorkshop on Challenges in Natural Language Processing for Clinical Data, p.121, 2011.

P. Jindal and D. Roth, Using Domain Knowledge and Domain-inspired Discourse Model for Coreference Resolution for Clinical Narratives, Journal of the American Medical Association, p.86, 2013.

M. Jixin and B. Knight, A General Temporal Theory, The Computer Journal (cit, p.16, 1994.

T. Joachims, T. Finley, and C. Yu, Cutting-Plane Training of Structural SVMs, Machine Learning 77.1, p.35, 2009.

A. E. Johnson, J. Tom, L. Pollard, . Shen, H. Li-wei et al., MIMIC-III, a Freely Accessible Critical Care Database, Scientiic Data, vol.3, p.44, 2016.

H. Jung and A. Stent, ATT1: Temporal Annotation Using Big Windows and Rich Syntactic and Semantic Features, Proceedings of the 7th International fiorkshop on Semantic Evaluation, pp.20-24, 2013.

H. Kamp and U. Reyle, From Discourse to Logic. Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory, p.71, 1993.
DOI : 10.1007/978-94-011-2066-1

L. Karttunen, Discourse Referents". In: Syntax and Semantics. Ed. by James D. McCawley, vol.7, p.73, 1976.

G. Katz and F. Arosio, The Annotation of Temporal Information in Natural Language Sentences, Proceedings of the ACL 2001 fiorkshop on Temporal and Spatial Information Processing, p.22, 2001.

A. Kehler, D. Appelt, L. Taylor, and A. Simma, The (Non) Utility of Predicate-Argument Frequencies for Pronoun Interpretation, Proceedings of the 2004 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.81, 2004.

A. Khalifa, S. Velupillai, and S. Meystre, UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text, Proceedings of the 10th International fiorkshop on Semantic Evaluation, vol.49, pp.35-37, 2016.

J. -. Kim, T. Dong, S. Ohta, Y. Pyysalo, J. Kano et al., Overview of BioNLP'09 Shared Task on Event Extraction, Proceedings of BioNLP 2009 fiorkshop, p.20, 2009.

J. Kim, S. Pyysalo, T. Ohta, R. Bossy, N. Nguyen et al., Overview of BioNLP Shared Task, Association for Computational Linguistics, p.20, 2011.

J. Kim, Y. Wang, N. Colic, H. Seung, Y. H. Beak et al., Refactoring the Genia Event Extraction Shared Task Toward a General Framework for IE-Driven KB Development, Proceedings of the 15th fiorkshop on Biomedical Natural Language Processing, p.20, 2016.

J. Kim, Y. Wang, and Y. Yasunori, The Genia Event Extraction Shared Task, Proceedings of the 2013 BioNLP fiorkshop, p.20, 2013.

D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, Proceedings of the 3rd International Conference on Learning Representations, p.66, 2015.

M. Klenner, Enforcing Consistency on Coreference Sets, Proceedings of the International Conference on Recent Advances in Natural Language Processing, p.81, 2007.

O. Kolomiyets and M. Moens, KUL: Data-driven Approach to Temporal Parsing of Newswire Articles, Proceedings of the 7th International fiorkshop on Semantic Evaluation, p.35, 2013.

A. Kolya, A. Kumar, R. Kundu, A. Gupta, S. Ekbal et al., JU_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal Relations, Proceedings of the 7th International fiorkshop on Semantic Evaluation, p.35, 2013.

A. Kova?evi?, A. Dehghan, M. Filannino, J. A. Keane, and G. Nenadic, Combining Rules and Machine Learning for Extraction of Temporal Expressions and Events from Clinical Narratives, Journal of the American Medical Informatics Association, vol.20, p.36, 2013.

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, vol.118, p.107, 2016.
DOI : 10.18653/v1/n16-1030
URL : https://doi.org/10.18653/v1/n16-1030

F. Landragin, A. D. Angeli, F. Woll, P. Lopez, and L. Romary, Information Sharing: Reference and Presupposition in Language Generation and Interpretation, p.71, 2002.

S. Lappin and H. J. Leass, An Algorithm for Pronominal Anaphora Resolution, Computational Linguistics 20, vol.4, p.79, 1994.

E. Lassalle and P. Denis, Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures, Proceedings of the 29th AAAI Conference on Artiicial Intelligence, vol.86, p.85, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01205189

H. Lee, H. Xu, J. Wang, Y. Zhang, S. Moon et al., UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes, Proceedings of the 10th International fiorkshop on Semantic Evaluation, vol.49, p.68, 2016.

H. Lee, Y. Peirsman, A. Chang, N. Chambers, M. Surdeanu et al., Stanford's Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task, Proceedings of the 15th Conference on Computational Natural Language Learning, p.78, 2011.

K. Lee, L. He, M. Lewis, and L. Zettlemoyer, End-to-end Neural Coreference Resolution, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017.
DOI : 10.18653/v1/d17-1018
URL : https://doi.org/10.18653/v1/d17-1018

A. Leeuwenberg and M. Moens, KULeuven-LIIR at SemEval2017 Task 12: Cross-Domain Temporal Information Extraction from Clinical Records, Proceedings of the 11th International fiorkshop on Semantic Evaluation, p.36, 2017.
DOI : 10.18653/v1/s17-2181
URL : https://doi.org/10.18653/v1/s17-2181

A. Leeuwenberg and M. Moens, Structured Learning for Temporal Relation Extraction from Clinical Records, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol.124, p.61, 2017.

C. Lin, D. Dligach, T. A. Miller, S. Bethard, and G. K. Savova, Multilayered Temporal Modeling for the Clinical Domain, Journal of the American Medical Informatics Association, vol.23, pp.387-395, 2016.

C. Lin, T. Miller, D. Dligach, S. Bethard, and G. Savova, Improving Temporal Relation Extraction with Training Instance Augmentation, Proceedings of the 15th fiorkshop on Biomedical Natural Language Processing, p.61, 2016.

Y. Lin, H. Chen, and R. A. Brown, MedTime: A Temporal Information Extraction System for Clinical Narratives, Journal of Biomedical Informatics, vol.46, pp.20-28, 2013.

, The ACE 2005 (ACE05) Evaluation Plan". In: (cit, p.20, 2005.

H. Liu, T. Christiansen, W. A. Baumgartner, and K. Verspoor, BioLemmatizer: A Lemmatization Tool for Morphological Processing of Biomedical Text, Journal of Biomedical Semantics, vol.3, issue.1, p.45, 2012.

H. Llorens, L. Derczynski, R. Gaizauskas, and E. Saquete, TIMEN: An Open Temporal Expression Normalisation Resource, Proceedings of the 8th International Conference on Language Resources and Evaluation, p.34, 2012.

J. Lu and V. Ng, Event Coreference Resolution: A Survey of Two Decades of Research, Proceedings of the 27th International Joint Conference on Artiicial Intelligence, pp.5479-5486, 2018.

X. Luo, On Coreference Resolution Performance Metrics, Proceedings of the 2005 Conference on Empirical Methods in Natural Language Processing, pp.25-32, 2005.
DOI : 10.3115/1220575.1220579

X. Luo, A. Ittycheriah, H. Jing, N. Kambhatla, and S. Roukos, A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree, Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, pp.83-85, 2004.

C. Ma, J. Rao-doppa, J. W. Orr, P. Mannem, X. Fern et al., Prune-and-Score: Learning for Greedy Coreference Resolution, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, p.84, 2014.
DOI : 10.3115/v1/d14-1225
URL : https://doi.org/10.3115/v1/d14-1225

X. Ma and E. Hovy, End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp.1064-1074, 2016.

S. Macavaney, A. Cohan, and N. Goharian, GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction, Proceedings of the 11th International fiorkshop on Semantic Evaluation, p.68, 2017.

. Mani, B. Inderjeet, J. Schiiman, and . Zhang, Inferring Temporal Ordering of Events in News, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.36, 2003.

. Mani, M. Inderjeet, B. Verhagen, C. M. Wellner, J. Lee et al., Machine Learning of Temporal Relations, Proceedings of the 21st International References Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, p.36, 2006.

I. Mani and G. Wilson, Robust Temporal Processing of News, Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, vol.34, p.11, 2000.

C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard et al., Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp.55-60, 2014.

O. March and T. Baldwin, Australasian Language Technology Association, Proceedings of the 2008 Australasian Language Technology Association fiorkshop, p.35, 2008.

G. Mar?ic, Temporal Processing of News: Annotation of Temporal Expressions, Verbal Events and Temporal Relations, pp.11-15, 2011.

S. Martschat, Structured Representations for Coreference Resolution, p.80, 2017.
DOI : 10.1162/tacl_a_00147
URL : https://doi.org/10.1162/tacl_a_00147

A. Mccallum and B. Wellner, Conditional Models of Identity Uncertainty with Application to Noun Coreference, Proceedings of the 2005 Neural Information Processing Systems Conference, p.81, 2005.

J. F. Mccarthy and W. G. Lehnert, Using Decision Trees for Coreference Resolution, Proceedings of the 1995 International Joint Conference on Artiicial Intelligence, pp.80-82, 1995.

D. Mcclosky, Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing, p.45, 2010.

A. T. Mccray, A. Burgun, and O. Bodenreider, Aggregating UMLS Semantic Types for Reducing Conceptual Complexity, Studies in Health Technology and Informatics, vol.1, p.40, 2001.

S. M. Meystre, K. Guergana, K. C. Savova, J. F. Kipper-schuler, and . Hurdle, Extracting Information from Textual Documents in the Electronic Health Record: a Review of Recent Research, Yearbook Of Medical Informatics, pp.128-144, 2008.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, EEcient Estimation of Word Representations in Vector Space, Computing Research Repository (arfliv) (cit, p.44, 2013.

T. Miller, S. Bethard, H. Amiri, and G. Savova, Unsupervised Domain Adaptation for Clinical Negation Detection, Proceedings of the 2017 BioNLP fiorkshop, p.64, 2017.

T. Miller, D. Dligach, S. Bethard, C. Lin, and G. Savova, Towards Generalizable Entity-Centric Clinical Coreference Resolution, In: Journal of Biomedical Informatics, vol.69, p.78, 2017.
DOI : 10.1016/j.jbi.2017.04.015
URL : http://europepmc.org/articles/pmc5508069?pdf=render

T. Mitamura, Z. Liu, and E. Hovy, Overview of TAC-KBP 2015 Event Nugget Track, Proceedings of the 2015 Text Analysis Conference, p.20, 2015.

M. Miwa and S. Ananiadou, Adaptable, High Recall, Event Extraction System with Minimal Connguration, BMC Bioinformatics, vol.16, issue.10, p.64, 2015.

M. Miwa and M. Bansal, End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp.1105-1116, 2016.

V. Moriceau and X. Tannier, French Resources for Extraction and Normalization of Temporal Expressions with HeidelTime, Proceedings of the 9th International Conference on Language Resources and Evaluation, p.34, 2014.

D. Mowery, H. Harkema, and W. Chapman, Temporal Annotation of Clinical Text, Proceedings of BioNLP 2008 fiorkshop, p.33, 2008.

. Neamatullah, M. M. Ishna, H. L. Douglass, A. Li-wei, M. Reisner et al., Automated De-identiication of Free-text Medical Records, BMC Medical Informatics and Decision Making, vol.8, issue.1, 2008.

M. Negri and L. Marseglia, Recognition and Normalization of Time Expressions: ITC-irst at TERN 2004. Research rep. Information Society Technologies (cit, p.34, 2005.

. Névéol, K. B. Aurélie, C. Cohen, T. Grouin, T. Hamon et al., Clinical Information Extraction at the CLEF eHealth Evaluation lab, CLEF eHealth Evaluation Lab, 2016.

A. Névéol, H. Dalianis, S. Velupillai, G. Savova, and P. Zweigenbaum, Clinical Natural Language Processing in Languages other than English: Opportunities and Challenges, In: Journal of Biomedical Semantics, vol.9, issue.1, p.40, 2018.

V. Ng, Supervised Noun Phrase Coreference Research: The First Fifteen Years, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, p.80, 2010.

V. Ng, Advanced Machine Learning Models for Coreference Resolution, Anaphora Resolution: Algorithms, Resources, and Applications, vol.83, p.80, 2016.

V. Ng, Machine Learning for Entity Coreference Resolution: A Retrospective Look at Two Decades of Research, Proceedings of the 31st AAAI Conference on Artiicial Intelligence, vol.88, p.80, 2017.

V. Ng and C. Cardie, Combining Sample Selection and Error-Driven Pruning for Machine Learning of Coreference Rules, Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, p.81, 2002.

V. Ng and C. Cardie, Identifying Anaphoric and Non-Anaphoric Noun Phrases to Improve Coreference Resolution, Proceedings of the 19th International Conference on Computational Linguistics, p.81, 2002.

V. Ng and C. Cardie, Improving Machine Learning Approaches to Coreference Resolution, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp.81-83, 2002.

C. Nicolae and G. Nicolae, BestCut: A Graph Algorithm for Coreference Resolution, Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, p.81, 2006.

A. Nikfarjam, E. Emadzadeh, and G. Gonzalez, Towards Generating a Patient's Timeline: Extracting Temporal Relationships from Clinical Notes, Journal of Biomedical Informatics, vol.46, p.37, 2013.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic Diierentiation in PyTorch, Proceedings of the NIPS 2017 Autodii fiorkshop, p.113, 2017.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol.12, p.45, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

N. Peng, H. Poon, C. Quirk, K. Toutanova, and W. Yih, Cross-Sentence N-ary Relation Extraction with Graph LSTMs, Transactions of the Association for Computational Linguistics 5, p.125, 2017.

J. F. Penz, B. Adam, J. F. Wilcox, and . Hurdle, Automated Identiication of Adverse Events Related to Central Venous Catheters, Journal of Biomedical Informatics, vol.40, pp.174-182, 2007.

J. P. Pestian, C. Brew, P. Matykiewicz, D. J. Hovermale, N. Johnson et al., A Shared Task Involving Multi-label Classiication of Clinical Free Text, Proceedings of BioNLP 2007 fiorkshop, pp.97-104, 2007.

M. Poesio, Linguistic and Cognitive Evidence About Anaphora, Anaphora Resolution: Algorithms, Resources, and Applications, pp.71-74, 2016.

M. Poesio and R. Stuckardt, Anaphora Resolution: Algorithms, Resources, and Applications, vol.83, p.80, 2016.

M. Poesio, R. Stuckardt, Y. Versley, and R. Vieira, Early Approaches to Anaphora Resolution: Theoretically Inspired and Heuristic based, Anaphora Resolution: Algorithms, Resources, and Applications, p.79, 2016.

M. Poesio and R. Vieira, A Corpus-based Investigation of Deenite Description Use, Computational Linguistics, vol.24, issue.2, p.74, 1998.

S. Ponzetto, M. Paolo, and . Strube, Exploiting Semantic Role Labeling, WordNet and Wikipedia for Coreference Resolution, Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.82, p.81, 2006.

S. Pradhan, X. Luo, M. Recasens, E. Hovy, V. Ng et al., Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp.30-35, 2014.

. Pradhan, A. Sameer, N. Moschitti, O. Xue, Y. Uryupina et al., CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp.1-40, 2012.

. Pradhan, L. Sameer, M. Ramshaw, M. Marcus, R. Palmer et al., CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes, Proceedings of the 15th Conference on Computational Natural Language References Learning, pp.1-27, 2011.

. Pradhan, L. Sameer, R. Ramshaw, J. Weischedel, L. Macbride et al., Unrestricted Coreference: Identifying Entities and Events in OntoNotes, Proceedings of the First IEEE International Conference on Semantic Computing, pp.446-453, 2007.
DOI : 10.1109/icsc.2007.93

J. Pustejovsky, The Generative Lexicon, p.12, 1995.

J. Pustejovsky, P. Hanks, R. Sauri, A. See, R. Gaizauskas et al., The TIMEBANK Corpus". In: Corpus Linguistics, 2003.

J. Pustejovsky, B. Ingria, R. Sauri, J. Castano, J. Littman et al., The speciication language TimeML, The Language of Time: A Reader, pp.545-557, 2005.

J. Pustejovsky, K. Lee, H. Bunt, and L. Romary, ISO-TimeML: An International Standard for Semantic Annotation, Proceedings of the 7th International Conference on Language Resources and Evaluation, vol.26, pp.394-397, 2010.

J. Pustejovsky and A. Stubbs, Increasing Informativeness in Temporal Annotation, Proceedings of the 5th Linguistic Annotation fiorkshop, p.23, 2011.

J. Quinlan and . Ross, C4.5: Programs for Machine Learning, pp.80-82, 1993.

A. Rahman and V. Ng, Supervised Models for Coreference Resolution, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processin, pp.968-977, 2009.
DOI : 10.3115/1699571.1699639
URL : http://dl.acm.org/ft_gateway.cfm?id=1699639&type=pdf

A. Rahman and V. Ng, Coreference Resolution with World Knowledge, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, p.81, 2011.

A. Rahman and V. Ng, Narrowing the Modeling Gap: A Cluster-Ranking Approach to Coreference Resolution, Journal of Artiicial Intelligence Research, vol.40, pp.469-521, 2011.

M. Recasens and E. Hovy, A Deeper Look into Features for Coreference Resolution, Proceedings of the 7th Discourse Anaphora and Anaphor Resolution Colloquium, p.81, 2009.
DOI : 10.1007/978-3-642-04975-0_3
URL : http://www.isi.edu/natural-language/people/hovy/papers/09DAARC-coref-final.pdf

R. Recasens, M. , and E. Hovy, BLANC: Implementing the Rand Index for Coreference Evaluation, Natural Language Engineering, vol.17, pp.485-510, 2011.

M. Recasens, E. Hovy, and M. A. Martí, Identity, Non-Identity, and Near-Identity: Addressing the Complexity of Coreference, Lingua 121, p.73, 2011.

R. ?eh??ek and P. Sojka, Software Framework for Topic Modelling with Large Corpora, Proceedings of the 7th International Conference on Language Resources and Evaluation, p.44, 2010.

M. Rei, Semi-supervised Multitask Learning for Sequence Labeling, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, p.110, 2017.
DOI : 10.18653/v1/p17-1194
URL : https://doi.org/10.18653/v1/p17-1194

H. Reichenbach, Elements of Symbolic Logic. The Macmillan Company (cit, p.16, 1947.

N. Reimers and I. Gurevych, Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, vol.117, p.113, 2017.

A. Roberts, R. Gaizauskas, G. Demetriou, Y. Guo, A. Setzer et al., Semantic Annotation of Clinical Text: The CLEF Corpus, Proceedings of the LREC 2008 fiorkshop on Building and Evaluating Resources for Biomedical Text Mining, p.29, 2008.

A. Roberts, R. Gaizauskas, M. Hepple, G. Demetriou, Y. Guo et al., Building a Semantically Annotated Corpus of Clinical Texts, Journal of Biomedical Informatics, vol.42, p.26, 2009.

K. Roberts, B. Rink, and S. M. Harabagiu, A Flexible Framework for Recognizing Events, Temporal Expressions, and Temporal Relations in Clinical Text, Journal of the American Medical Informatics Association, vol.20, pp.34-36, 2013.

R. Saurí, J. Littman, B. Knippen, R. Gaizauskas, A. Setzer et al., TimeML Annotation Guidelines: ffersion 1.2.1. Tech. rep. (cit, vol.25, p.24, 2006.

G. K. Savova, S. Bethard, W. Styler, J. Martin, M. Palmer et al., Towards Temporal Relation Discovery from the Clinical Narrative, Proceedings of the 2009 AMIA Annual Symposium, p.33, 2009.

G. K. Savova, W. W. Chapman, J. Zheng, and R. S. Crowley, Anaphoric Relations in the Clinical Narrative: Corpus Creation, Journal of the American Medical Informatics Association, vol.18, p.77, 2011.

G. K. Savova, J. James, P. V. Masanz, J. Ogren, S. Zheng et al., Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, Component Evaluation and Applications, Journal of the American Medical Informatics Association, vol.17, pp.507-513, 2010.

A. Setzer, Temporal Information in Newswire Articles: an Annotation Scheme and Corpus Study, p.22, 2001.

A. Setzer and R. Gaizauskas, On the Importance of Annotating EventEvent Temporal Relations in Text, Proceedings of the LREC 2002 fiorkshop on Annotation Standards for Temporal Information in Natural Language, p.22, 2002.

W. Soon, H. T. Meng, D. Ng, and . Lim, A Machine Learning Approach to Coreference Resolution of Noun Phrases, Computational Linguistics 27, vol.4, pp.80-84, 2001.

V. Stoyanov and J. Eisner, Easy-First Coreference Resolution, Proceedings of the 24th International Conference on Computational Linguistics, pp.2519-2534, 2012.

. Stoyanov, N. Veselin, C. Gilbert, E. Cardie, and . Riloo, Conundrums in Noun Phrase Coreference Resolution: Making Sense of the State-of-the-Art, Proceedings of the Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, pp.656-664, 2009.

J. Strötgen and M. Gertz, Multilingual and Cross-domain Temporal Tagging, Language Resources and Evaluation 47, vol.2, p.34, 2013.

J. Strötgen and M. Gertz, A Baseline Temporal Tagger for All Languages, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, p.17, 2015.

J. Strötgen and M. Gertz, Domain-Sensitive Temporal Tagging. Synthesis Lectures on Human Language Technologies (cit, vol.18, p.17, 2016.

M. Strube, Never Look Back: An Alternative to Centering, Proceedings of the 17st International Conference on Computational Linguistics and the 36th Annual Meeting of the Association for Computational Linguistics, p.79, 1998.

M. Strube and U. Hahn, Functional Centering Grounding Referential Coherence in Information Structure, Computational Linguistics 25.3, p.79, 1999.

A. Stubbs and Ö. Uzuner, Annotating Longitudinal Clinical Narratives for De-Identiication: The 2014 i2b2/UTHealth Corpus, Journal of Biomedical Informatics, vol.58, pp.20-29, 2015.

I. V. Styler, F. William, S. Bethard, S. Finan, M. Palmer et al., Temporal Annotation in the Clinical Domain, vol.19, p.64, 2014.

I. V. Styler, F. William, G. Savova, M. Palmer, J. Pustejovsky et al., THYME Annotation Guidelines. Tech. rep, p.26, 2014.

F. M. Suchanek, G. Kasneci, and G. Weikum, Yago: A Core of Semantic Knowledge, Proceedings of the 16th International fiorld fiide fieb Conference, p.81, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01472497

W. Sun, A. Rumshisky, and O. Uzuner, Annotating Temporal Information in Clinical Narratives, Journal of Biomedical Informatics, vol.46, pp.5-12, 2013.

W. Sun, A. Rumshisky, and O. Uzuner, Evaluating Temporal Relations in Clinical Text: 2012 i2b2 Challenge, Journal of the American Medical Informatics Association, vol.20, pp.806-813, 2013.

W. Sun, A. Rumshisky, and O. Uzuner, Temporal Reasoning over Clinical Text: The State of the Art, Journal of the American Medical Informatics Association, vol.20, pp.814-819, 2013.

. Sun, T. Xu, D. Matsuzaki, J. Okanohara, and . Tsujii, Latent Variable Perceptron Algorithm for Structured Classiication, Proceedings of the 21st International Joint Conference on Artiicial Intelligence, p.85, 2009.

B. M. Sundheim, Tipster/MUC-5: Information Extraction System Evaluation, Proceedings of the 5th Message Understanding Conference, pp.147-163, 1993.

B. M. Sundheim, Overview of Results of the MUC-6 Evaluation, Proceedings of the 6th Message Understanding Conference, vol.423, 1995.

T. Nzali, M. Donald, X. Tannier, and A. Névéol, Automatic Extraction of Time Expressions Accross Domains in French Narratives, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.492-498, 2015.

J. Tourille, M. Doutreligne, O. Ferret, N. Paris, A. Névéol et al., Evaluation of a Sequence Tagging Tool for Biomedical Texts, Proceedings of the 9th International fiorkshop on Health Text Mining and Information Analysis, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01990502

J. Tourille, O. Ferret, A. Névéol, and X. Tannier, Extraction de Relations Temporelles dans des Dossiers Électroniques Patient, Actes de la Conférence Traitement Automatique des Langues Naturelles, pp.459-466, 2016.

J. Tourille, O. Ferret, A. Névéol, and X. Tannier, LIMSI-COT at SemEval-2016 Task 12: Temporal Relation Identiication Using a Pipeline of Classiiers, Proceedings of the 10th International fiorkshop on Semantic Evaluation, pp.1136-1142, 2016.

J. Tourille, O. Ferret, X. Tannier, and A. Névéol, LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives, Proceedings of the 11th International fiorkshop on Semantic Evaluation, pp.597-602, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01857879

J. Tourille, O. Ferret, X. Tannier, and A. Névéol, Neural Architecture for Temporal Relation Extraction: A Bi-LSTM Approach for Detecting Narrative Containers, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp.224-230, 2017.
URL : https://hal.archives-ouvertes.fr/cea-01841667

J. Tourille, O. Ferret, X. Tannier, and A. Névéol, Temporal Information Extraction from Clinical Text, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, pp.739-745, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01842460

Ö. Uzuner, Recognizing Obesity and Comorbidities in Sparse Data, Journal of the American Medical Informatics Association, vol.16, pp.561-570, 2009.

Ö. Uzuner, A. Bodnari, S. Shen, T. Forbush, J. Pestian et al., Evaluating the State of the Art in Coreference Resolution for Electronic Medical Records, Journal of the American Medical Informatics Association, vol.19, pp.786-791, 2012.

Ö. Uzuner, I. Goldstein, Y. Luo, and I. Kohane, Identifying Patient Smoking Status from Medical Discharge Records, Journal of the American Medical Informatics Association, vol.15, issue.1, pp.14-24, 2008.

Ö. Uzuner, Y. Luo, and P. Szolovits, Evaluating the State-of-the-Art in Automatic De-identiication, Journal of the American Medical Informatics Association, vol.14, pp.550-563, 2007.

Ö. Uzuner, I. Solti, and E. Cadag, Extracting Medication Information from Clinical Text, Journal of the American Medical Informatics Association, vol.17, pp.514-518, 2010.

. Uzuner, B. R. Özlem, S. South, S. L. Shen, and . Duvall, 2010 i2b2/VA Challenge on Concepts, Assertions, and Relations in Clinical Text, Journal of the American Medical Informatics Association, vol.18, pp.552-556, 2011.

N. Uzzaman, H. Llorens, L. Derczynski, J. Allen, M. Verhagen et al., SemEval-2013 Task 1: TempEval-3-Evaluating Time Expressions, Events, and Temporal Relations, Proceedings of the 7th International fiorkshop on Semantic Evaluation, pp.1-9, 2013.

. Velupillai, D. Sumithra, B. R. Mowery, M. South, H. Kvist et al., Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis, Yearbook of Medical Informatics, vol.10, issue.1, pp.183-192, 2015.

. Velupillai, D. L. Sumithra, S. Mowery, L. Abdelrahman, W. Christensen et al., BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge, Proceedings of the 9th International fiorkshop on Semantic Evaluation, pp.815-819, 2015.

Z. Vendler, Linguistics in Philosophy, pp.12-14, 1967.

M. Verhagen, Times Between the Lines, p.17, 2004.

M. Verhagen, R. Gaizauskas, F. Schilder, M. Hepple, G. Katz et al., SemEval-2007 Task 15: TempEval Temporal Relation Identiication, Proceedings of the 4th International fiorkshop on Semantic Evaluation, pp.75-80, 2007.

M. Verhagen, R. Saur??, T. Saur??, J. Caselli, and . Pustejovsky, Association for Computational Linguistics, Proceedings of the 5th International fiorkshop on Semantic Evaluation, 2010.

M. Vilain, J. Burger, J. Aberdeen, D. Connolly, and L. Hirschman, A Model-Theoretic Coreference Scoring Scheme, Proceedings of the 6th Message Understanding Conference, vol.87, pp.45-52, 1995.

X. Wang, G. Hripcsak, M. Markatou, and C. Friedman, Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and ElecReferences tronic Health Records: A Feasibility Study, Journal of the American Medical Informatics Association, vol.16, issue.3, pp.328-337, 2009.

K. Webster and J. R. Curran, Limited Memory Incremental Coreference Resolution, Proceedings of the 25th International Conference on Computational Linguistics, p.84, 2014.

. Wiseman, A. M. Sam, S. M. Rush, and . Shieber, Learning Global Features for Coreference Resolution, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.117, pp.994-1004, 2016.

. Wiseman, A. M. Sam, S. Rush, J. Shieber, and . Weston, Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution, Proceedings of the Joint Conference of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, pp.1416-1426, 2015.

Y. Xu, J. Liu, J. Wu, Y. Wang, Z. Tu et al., A Classiication Approach to Coreference in Discharge Summaries: 2011 i2b2 Challenge, Journal of the American Medical Informatics Association, vol.19, p.81, 2012.

Y. Xu, Y. Wang, T. Liu, J. Tsujii, and E. Chang, An End-toEnd System to Identify Temporal Relation in Discharge Summaries: 2012 i2b2 Challenge, Journal of the American Medical Informatics Association, vol.20, p.37, 2013.

X. Yang, J. Su, J. Lang, C. Lim-tan, T. Liu et al., An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, p.84, 2008.

X. Yang, G. Zhou, J. Su, and C. Tan, Coreference Resolution Using Competition Learning Approach, Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp.176-183, 2003.

K. Yoshikawa, S. Riedel, M. Asahara, and Y. Matsumoto, Jointly Identifying Temporal Relations with Markov Logic, Proceedings of the Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, p.38, 2009.

C. Yu, T. John, and . Joachims, Learning Structural SVMs with Latent Variables, Proceedings of the 26th International Conference on Machine Learning, vol.86, p.85, 2009.

Y. Zhang, B. Tang, M. Jiang, J. Wang, and H. Xu, Domain Adaptation for Semantic Role Labeling of Clinical Text, Journal of the American Medical Informatics Association, vol.22, p.64, 2015.

. Zheng, W. W. Jiaping, R. S. Chapman, G. K. Crowley, and . Savova, Coreference Resolution: A Review of General Methodologies and Applications in the Clinical Domain, Journal of Biomedical Informatics, vol.44, p.80, 2011.

, Appendix C i2b2 Task 1c Corpus : Conversion to Brat and CoNLL Formats #begin document, p.587

C. Figure, 2: i2b2 task1c corpus: CoNLL formatted sentence example