S. P. Abney, Parsing By Chunks, Principle-Based Parsing: Computation and Psycholinguistics, p.105, 1992.

L. Ackerman, Syntactic and cognitive issues in investigating gendered coreference, Glossa, p.56, 2019.

P. Amsili and O. Seminck, Schémas Winograd en français: une étude statistique et comportementale'. In: Actes de la 24e Conférence sur le Traitement Automatique des Langues Naturelles, 2017.

J. Antoine, Résolutions des anaphores pronominales : quelques postulats du TALN mis à l'épreuve du dialogue oral finalisé'. In: Actes de la 11ème Conférence sur le Traitement Automatique des Langues Naturelles, Association pour le Traitement Automatique des Langues, 2004.

C. Aone and S. William, Evaluating Automated and Manual Acquisition of Anaphora Resolution Strategies, 33rd Annual Meeting of the Association for Computational Linguistics. ACL, pp.122-129, 1995.

I. Asimov, I, Robot. Gnome (cit, p.1, 1950.

J. Ba, J. R. Lei, G. E. Kiros, and . Hinton, Layer Normalization, vol.82, p.81, 2016.

A. Bagga and B. Baldwin, Algorithms for Scoring Coreference Chains, Proceedings of the First International Conference on Language Resources and Evaluation. Workshop on Linguistics Coreference. Granada, España: European Language Resource Association, p.23, 1998.

A. Bagga and B. Baldwin, Entity-Based Cross-Document Coreferencing Using the Vector Space Model, Proceedings of COLING 1998: The 17th International Conference on Computational Linguistics. ACL-COLING 1998, vol.1, 1998.

J. Balazs and Y. Matsuo, Gating Mechanisms for Combining Character and Word-level Word Representations: an Empirical Study, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 2019.

, Student Research Workshop, p.79, 2019.

B. Baldwin, T. Morton, A. Bagga, J. Baldridge, R. Chandraseker et al., Description of the UPENN CAMP System as Used for Coreference, Seventh Message Understanding Conference, p.31, 1998.

M. Ballesteros, C. Dyer, and N. A. Smith, Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, p.71, 2015.

P. Ba?ski, B. Gaiffe, P. Lopez, S. Meoni, L. Romary et al., Wake up, standOff!' Sept, p.44, 2016.

C. Barras, E. Geoffrois, Z. Wu, and M. Liberman, Transcriber: a Free Tool for Segmenting, Labeling and Transcribing Speech, Proceedings of the First International Conference on Language Resources and Evaluation. LREC, 1998.

. European-language-resources, , 1998.

O. Baude and C. Dugua, Re)faire le corpus d'Orléans quarante ans après : quoi de neuf, linguiste ?, In: Corpus. Varia, vol.10, p.37, 2011.

M. Belkin, D. Hsu, S. Ma, and S. Mandal, Reconciling modern machine learning practice and the bias-variance trade-off, p.81, 2018.

E. M. Bender, url: https : / / thegradient . pub / thebenderrule -on -naming -the -languages -we -study -and -why -it -matters, The Gradient (15th Sept. 2019), 2019.

Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, A neural probabilistic language model, The Journal of Machine Learning Research, vol.3, pp.1137-1155, 2003.

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.85, p.61, 2008.

J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, Algorithms for Hyper-Parameter Optimization, Advances in Neural Information Processing Systems. NeurIPS, vol.24, p.93, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00642998

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. ACL, 2014.

, , vol.1, p.57

B. Bohnet, R. Mcdonald, G. Simões, D. Andor, E. Pitler et al., Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. ACL 2018, vol.1, pp.2642-2652, 2018.

P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, Enriching Word Vectors with Subword Information, Transactions of the Association for Computational Linguistics, vol.5, pp.135-146, 2017.

M. Brassier, A. Puret, A. Voisin-marras, and L. Grobol, Classification par paires de mention pour la résolution des coréférences en français parlé interactif, Association pour le Traitement Automatique des Langues, p.67, 2018.

S. Broscheit, M. Poesio, S. P. Ponzetto, K. Rodriguez, L. Romano et al., BART: A Multilingual Anaphora Resolution System, Proceedings of the 5th International Workshop on Semantic Evaluation. SemEval, pp.104-107, 2010.

G. Brown and G. Yule, Discourse Analysis, 1983.

P. F. Brown, J. D. Vincent, P. V. Pietra, J. C. Desouza, R. L. Lai et al., Class-Based n-gram Models of Natural Language, Computational Linguistics 18, vol.4, p.99, 1992.

F. Bruneseaux and L. Romary, Proceedings of the Joint International Conference of the Association for Computers and the Humanities and the Association for Literary & Linguistic Computing. ACH-ALLC '97, pp.3-7, 1997.

M. Buda, A. Maki, and M. A. Mazurowski, A systematic study of the class imbalance problem in convolutional neural networks', Neural Networks, vol.106, p.83, 2018.

L. Burnard, What is the Text Encoding Initiative? : How to add intelligent markup to digital resources, vol.114, p.43, 2014.

J. Cai and M. Strube, End-to-End Coreference Resolution via Hypergraph Partitioning, Proceedings of the 23rd International Conference on Computational Linguistics. COLING 2010, p.59, 2010.

N. Calzolari, The LREC Map of Language Resources and Technologies, Proceedings of the Seventh International Conference on Language Resources and Evaluation. LREC 2010, p.35, 2010.

M. Candito and D. Seddah, Le corpus Sequoia : annotation syntaxique et exploitation pour l'adaptation d'analyseur par pont lexical, Actes de la conférence, vol.2, p.103, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00698938

M. Chen and . Xu, The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. ACL 2018, vol.1, p.83, 2018.

S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran et al., cuDNN: Efficient Primitives for Deep Learning, p.89, 2014.

N. Chinchor, MUC-4 Evaluation Metrics, 4th Message Understanding Conference, p.20, 1992.

K. Cho, B. Van-merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares et al., Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp.1724-1734, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01433235

H. Choi, K. Cho, and Y. Bengio, Context-dependent word representation for neural machine translation, Computer Speech & Language, p.101, 2017.

N. Chomsky, Lectures on Government and Binding: The Pisa Lectures. Studies in Generative Grammar, vol.106, p.56, 1981.

K. Clark and C. D. Manning, Entity-Centric Coreference Resolution with Model Stacking, Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. ACL-IJCNLP 2015 (???, ??, vol.1, pp.15-1136, 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. ACL 2016, pp.7-12, 2016.

C. Clouzot, G. Antoniadis, and A. Tutin, Constitution and exploitation of an annotation system of electronic corpora: Toward automatic generation of understandable pronouns in French language, Natural Language Processing -NLP 2000. Ed. by Dimitris N. Christodoulakis. Lecture Notes in Computer Science, p.36, 2000.

R. Collobert and J. Weston, A unified architecture for natural language processing: deep neural networks with multitask learning, Proceedings of the 25th international conference on Machine learning. ICML, pp.160-167, 2008.

R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu et al., Natural Language Processing (almost) from Scratch, Journal of Machine Learning Research, vol.12, pp.2493-2537, 2011.

A. Cornuéjols and L. Miclet, Apprentissage artificiel -Concepts et algorithmes, 2010.

. Crabbé, M. Benoit, C. Fabre, and . Pallier, Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. EMNLP-IJCNLP 2019, pp.1150-1160, 2019.

J. Cross and L. Huang, Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp.1-11, 2016.

A. Culotta, M. Wick, and A. Mccallum, First-Order Probabilistic Models for Coreference Resolution, The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference. NAACL-HLT, pp.81-88, 2007.

A. M. Dai, V. Quoc, and . Le, Semi-supervised Sequence Learning, Advances in Neural Information Processing Systems. NeurIPS, vol.28, p.101, 2015.

M. Delaborde and F. Landragin, En quoi le pronom « on » a-t-il une valeur anaphorique ? Le cas des successions d'occurrences de « on, Les cahiers de praxématique. La gestion de l'anaphore en discours : complexités et enjeux 72, pp.1-18, 2019.
URL : https://hal.archives-ouvertes.fr/halshs-01795213

P. Denis and J. Baldridge, A Ranking Approach to Pronoun Resolution, Proceddings of the 20th International Joint Conference on Artifical Intelligence. IJCAI, p.77, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00514931

P. Denis and J. Baldridge, Joint Determination of Anaphoricity and Coreference Resolution using Integer Programming'. In: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference. NAACL-HLT 2007, pp.236-243, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00514932

P. Denis and J. Baldridge, Specialized models and ranking for coreference resolution, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, p.660, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00514368

P. Denis and J. Baldridge, Global joint models for coreference resolution and named entity classification, Procesamiento del lenguaje natural 42, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00514302

A. Désoyer, F. Landragin, and I. Tellier, Apprentissage automatique d'un modèle de résolution de la coréférence à partir de données orales transcrites du français : le système CROC, Actes de la 22ème Conférence sur le Traitement Automatique des Langues Naturelles. TALN-RÉCITAL 2015, pp.439-445, 2015.

A. Désoyer, F. Landragin, I. Tellier, A. Lefeuvre, and J. Antoine, Les coréférences à l'oral : une expérience d'apprentissage automatique sur le corpus ANCOR, Traitement Automatique des Langues. Traitement automatique du langage parlé, vol.55, pp.97-121, 2015.

J. Devlin, M. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018.

L. R. Dice, Measures of the Amount of Ecologic Association Between Species'. In: Ecology 26, vol.3, p.24, 1945.

R. Diestel, Graph Theory. 5th ed. Graduate Texts in Mathematics, 2017.

M. Dinarelli and L. Grobol, Modélisation d'un contexte global d'étiquettes pour l'étiquetage de séquences dans les réseaux neuronaux récurrents'. In: Actes de la onzième édition de la plate-forme Intelligence Artificielle, p.71, 2018.

M. Dinarelli and L. Grobol, Hybrid Neural Networks for Sequence Modelling : The Best of Three Worlds, Actes de la 26ème Conférence sur le Traitement Automatique des Langues. TALN-RECITAL 2019, p.71, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02157160

G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel et al., The Automatic Content Extraction (ACE) Program : Tasks, Data, and Evaluation, Proceedings of the Fourth International Conference on Language Resources and Evaluation. LREC-2004, vol.18, pp.30-32, 2004.

T. Dozat, Incorporating Nesterov Momentum into Adam, Fourth International Conference on Learning Representations, 2016.

, /forum?id=OM0jvwB8jIp57ZJjtNEZ, p.80

T. Dozat and C. D. Manning, Deep Biaffine Attention for Neural Dependency Parsing, 5th International Conference on Learning Representations. 5th International Conference on Learning Representations, p.77, 2017.

Y. Dupont, La structuration dans les entités nommées, 2017.

G. Durrett, D. Hall, and D. Klein, Decentralized Entity-Level Modeling for Coreference Resolution, Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol.1, p.61, 2013.

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. EMNLP 2013. Association for Computational Linguistics, vol.60, pp.52-54, 2013.

C. Dyer, A. Kuncoro, M. Ballesteros, and N. A. Smith, Recurrent Neural Network Grammars, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. NAACL: HLT 2016. NAACL: HLT 2016, pp.199-209, 2016.

J. Edmonds, Optimum branchings, Journal of Research of the national Bureau of Standards B, vol.71, p.62, 1967.

S. Eger, P. Youssef, and I. Gurevych, Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks, 2018 Conference on Empirical Methods in Natural Language Processing, pp.4415-4424, 2018.

M. Ehrmann, Named entities, from Linguistics to NLP: Theoretical status and disambiguation methods, vol.16, p.15, 2008.
URL : https://hal.archives-ouvertes.fr/tel-01639190

. Eshkol-taravella, O. Iris, D. Baude, L. Maurel, C. Hriba et al., Un grand corpus oral « disponible » : le corpus d'Orléans, Traitement Automatique des Langues. Ressources Linguistiques Libres 53, vol.1, p.36, 2011.

S. Evert and A. Hardie, Twenty-first century Corpus Workbench: Updating a query architecture for the new millennium, Proceedings of the Corpus Linguistics 2011 conference. CL2011. Birmingham, United Kingdom, p.47, 2011.

G. Fauconnier, G. Lakoff, and E. Sweester, Mental spaces: aspects of meaning construction in natural language, p.9, 1994.

E. Fernandes, C. Dos-santos, and R. Milidiú, Latent Structure Perceptron with Feature Induction for Unrestricted Coreference Resolution, Proceedings of the 2012, 2012.

, Natural Language Processing and Computational Natural Language Learning. EMNLP-CoNLL, 2012.

E. R. Fernandes and U. Brefeld, Learning from Partially Annotated Sequences, Machine Learning and Knowledge Discovery in Databases, p.62, 2011.

E. Fernandes, C. Rezende, . Nogueira, R. Santos, and . Milidiú, Latent Trees for Coreference Resolution, Computational Linguistics, vol.40, pp.801-835, 2014.

J. Finkel, C. D. Rose, and . Manning, Enforcing Transitivity in Coreference Resolution, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies. HLT, p.23, 2008.

J. Finkel, C. D. Rose, and . Manning, Joint Parsing and Named Entity Recognition, Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. HLR: NAACL 2009, p.33, 2009.

G. Flaubert, Salammbô, p.22, 1910.

K. Fort, A. Nazarenko, and R. Claire, Corpus Linguistics for the Annotation Manager, Corpus Linguistics, p.30, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00641571

K. Fort, A. Nazarenko, and S. Rosset, Modeling the Complexity of Manual Annotation Tasks: a Grid of Analysis, Proceedings of COLING 2012. COLING 2012, p.31, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00769631

R. French and . Matthew, Catastrophic forgetting in connectionist networks, Trends in Cognitive Sciences 3.4 (Apr. 1999), p.85, 1999.

A. Gaier and D. Ha, Weight Agnostic Neural Networks'. In: (10th, p.80, 2019.

Y. Gal and Z. Ghahramani, A Theoretically Grounded Application of Dropout in Recurrent Neural Networks, p.81, 2015.

. Galliano, G. Sylvain, L. Gravier, and . Chaubard, The ESTER 2 evaluation campaign for the rich transcription of French radio broadcasts, Proceedings of the 10th Annual Conference of the International Speech Communication Association. InterSpeech, p.106, 2009.

C. Gardent and H. Manuélian, Création d'un corpus annoté pour le traitement des descriptions définies, Traitement Automatique des Langues. Modèles et algorithmes pour la résolution d'anaphores 1, vol.46, pp.115-140, 2005.

, CGardent/publis/tal05-dede.pdf, p.36

D. Gillick, B. Favre, D. Hakkani-tür, B. Bohnet, Y. Liu et al., The ICSI/UTD Summarization System at TAC, Proceedings of the Text Analysis Conference workshop, p.33, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01194277

E. Godbert and B. Favre, Détection de coréférences de bout en bout en français'. In: Actes de la 24e Conférence sur le Traitement Automatique des Langues Naturelles, Association pour le Traitement Automatique des Langues, vol.67, p.65, 2017.

F. Godin, Improving and Interpreting Neural Networks for Word-Level Prediction Tasks in Natural Language Processing, vol.214, p.103, 2019.

Y. Goldberg and J. Nivre, A Dynamic Oracle for Arc-Eager Dependency Parsing, Proceedings of the 24th International Conference on Computational Linguistics. COLING 2012, pp.959-976, 2012.

E. Grave, P. Bojanowski, P. Gupta, A. Joulin, and T. Mikolov, Learning Word Vectors for 157 Languages, Proceedings of the 11th International Conference on Language Resources and Evaluation. LREC 2018, vol.87, p.99, 2018.

A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks, vol.18, pp.602-610, 2005.

G. Gravier, G. Adda, N. Paulson, M. Carré, A. Giraudel et al., The ETAPE corpus for the evaluation of speech-based TV content processing in the French language, LREC -Eighth international conference on Language Resources and Evaluation. Turkey, na, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00712591

H. Grice and . Paul, Studies in the Way of Words, vol.406, p.10, 1989.

L. Grobol, Neural Coreference Resolution with Limited Lexical Context and Explicit Mention Detection for Oral French', Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference. CRAC 2019, pp.8-14, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02151569

L. Grobol, F. Landragin, and S. Heiden, Interoperable annotation of (co)references in the Democrat project, by Harry Bunt. ACL Special Interest Group on Computational Semantics (SIGSEM) and ISO TC 37/SC 4 (Language Resources) WG, vol.2, p.41, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01583527

L. Grobol, F. Landragin, and S. Heiden, XML-TEI-URS: using a TEI format for annotated linguistic resources, CLARIN Annual Conference, p.41, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01827563

L. Grobol, I. Tellier, É. Villemonte-de-la-clergerie, M. Dinarelli, and F. Landragin, Apports des analyses syntaxiques pour la détection automatique de mentions dans un corpus de français oral, Actes de la 24e Conférence sur le Traitement Automatique des Langues Naturelles. TALN 2017, vol.89, p.68, 2017.

L. Grobol, I. Tellier, É. Villemonte-de-la-clergerie, M. Dinarelli, and F. Landragin, ANCOR-AS: Enriching the ANCOR Corpus with Syntactic Annotations, Proceedings of the 11th edition of the Language Resources and Evaluation Conference. LREC 2018, vol.107, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01744572

S. Grönroos, S. Virpioja, P. Smit, and M. Kurimo, Morfessor Flat-Cat: An HMM-Based Method for Unsupervised and Semi-Supervised Learning of Morphology, Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. COLING, p.99, 2014.

B. Habert, Détournements d'annotation : armer la main et le regard, Corpus: méthodologie et applications linguistiques. Ed. by Mireille Bilger. 1 vols, p.30, 2000.

B. Hachey, W. Radford, J. Nothman, M. Honnibal, and J. R. Curran, Evaluating Entity Linking with Wikipedia, Artificial Intelligence. Artificial Intelligence, Wikipedia and Semi-Structured Resources 194, pp.130-150, 2013.

A. Haghighi and D. Klein, Simple Coreference Resolution with Rich Syntactic and Semantic Features, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. EMNLP 2009 (Singapore), pp.1152-1161, 2009.

G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue et al., Learning from class-imbalanced data: Review of methods and applications, Expert Systems with Applications, vol.73, p.83, 2017.

S. Hanson, . José, Y. Lorien, and . Pratt, Comparing Biases for Minimal Network Construction with Back-propagation, Proceedings of the 1st International Conference on Neural Information Processing Systems. NIPS'88, p.82, 1988.

M. Hatmi, Named entity recognition in multimodal documents'. Theses. Université de Nantes, p.106, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01154811

S. Heiden, Manuel de TXM, Extension Annotation URS (Unité-Relation-Schéma) version 1.0. Manual. 3rd, p.47, 2019.

S. Heiden, J. Magué, and B. Pincemin, TXM : Une plateforme logicielle open-source pour la textométrie -conception et développement', 10th International Conference on the Statistical Analysis of Textual Data. JADT 2010, vol.2, p.40, 2010.

B. Heinzerling and M. Strube, Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ACL 2019, pp.273-291, 2019.

I. Hendrickx, G. Bouma, W. Daelemans, and V. Hoste, COREA: Coreference Resolution for Extracting Answers for Dutch'. In: Essential Speech and Language Technology for Dutch: Results by the STEVIN programme, Peter Spyns and Jan Odijk. Theory and Applications of Natural Language Processing, p.33, 2013.

E. Hinrichs, S. Kübler, K. Naumann, H. Telljohann, and J. Trushkina, Recent developments in linguistic annotations of the TüBa-D/Z treebank, Proceedings of the Third Workshop on Treebanks and Linguistic Theories. TLT 2004, p.32, 2004.

G. E. Hinton, Learning translation invariant recognition in a massively parallel networks, PARLE Parallel Architectures and Languages Europe, p.82, 1987.

G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, p.81, 2012.

L. Hirschman and N. Chinchor, Appendix F: MUC-7 Coreference Task Definition (version 3.0)'. In: Seventh Message Understanding Conference, 1998.

G. Hirst, Anaphora in Natural Language Understanding: A Survey. Lecture Notes in Computer Science, 1981.

J. R. Hobbs, Resolving Pronoun References, Readings in Natural Language Processing, pp.339-352, 1986.

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation 9, vol.8, pp.1735-1780, 1997.

M. Honnibal and I. Montani, spaCy 2: Natural language understanding with Bloom embeddings, p.103, 2019.

E. Hovy, M. Marcus, M. Palmer, L. Ramshaw, and R. Weischedel, OntoNotes: The 90% Solution, Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Compuational Linguistics. HLT NAACL 06, 2006.

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 2015.

A. Isard, D. Mckelvie, A. Mengel, and M. B. Møller, The MATE Workbench: A Tool for Annotating XML Corpora, Content-Based Multimedia Information Access. RIAO 00, vol.1, p.40, 2000.

. Iso/tc, ISO AWI 24617-9 Language resource management -Part 9 Semantic annotation framework (SemAF). Reference, p.44, 2017.

P. Jaccard, The Distribution of the Flora in the Alpine Zone, New Phytologist, vol.11, p.98, 1912.

M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer et al., SpanBERT: Improving Pre-training by Representing and Predicting Spans, vol.73, p.61, 2019.

M. Joshi, O. Levy, D. S. Weld, and L. Zettlemoyer, BERT for Coreference Resolution: Baselines and Analysis, vol.73, p.61, 2019.

N. P. Jouppi, In-Datacenter Performance Analysis of a Tensor Processing Unit, ACM SIGARCH Computer Architecture News, vol.45, issue.2, p.101, 2017.

D. Jurafsky and J. H. Martin, Speech and Language Processing, 2019.

B. Kantor and A. Globerson, Coreference Resolution with Entity Equalization, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ACL 2019, pp.673-677, 2019.

A. Karpathy and J. Johnson, CS231n Convolutional Neural Networks for Visual Recognition, Lecture notes. Lecture notes, 2019.

L. Karttunen, Discourse Referents'. In: Notes from the Linguistic Underground, Syntax and Semantics, vol.7, 1976.

A. Kent, M. M. Berry, F. U. Luehrs, and J. W. Perry, Machine literature searching VIII. Operational criteria for designing information retrieval systems, pp.93-101, 1955.

D. P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, International Conference on Learning Representations, p.80, 2014.

D. P. Kingma, T. Salimans, and M. Welling, Variational Dropout and the Local Reparameterization Trick, p.81, 2015.

M. Klenner, Enforcing consistency on coreference sets, Proceedings of Recent Advances in Natural Processing, p.61, 2007.

M. Kope? and M. Ogrodniczuk, Creating a Coreference Resolution System for Polish, Proceedings of the Eighth International Conference on Language Resources and Evaluation. LREC 2012. European Language Resources Association, p.52, 2012.

A. Krogh and J. A. Hertz, A Simple Weight Decay Can Improve Generalization, 4th International Conference on Neural Information Processing Systems, pp.950-957, 1991.

D. Krueger, Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations, p.81, 2016.

J. B. Kruskal, On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem, Proceedings of the American Mathematical Society, p.62, 1956.

H. Kuhn, The Hungarian method for the assignment problem, Naval Research Logistics Quarterly, vol.2, p.24, 1955.

J. K. Kummerfeld, M. Bansal, D. Burkett, and D. Klein, Mention Detection: Heuristics for the OntoNotes Annotations, Proceedings of the Fifteenth Conference on Computational Natural Language Learning. CoNLL, vol.34, p.32, 2011.

A. Lacheret, Rhapsodie: a Prosodic-Syntactic Treebank for Spoken French, Proceedings of the Ninth International Conference on Language Resources and Evaluation. LREC 2014 (tex.venue: Reykjavik, Ísland), vol.46, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00968959

J. Lafferty, A. Mccallum, and F. Pereira, Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, 18th International Conference on Machine Learning. ICML '01, vol.655813, pp.282-289, 2001.

F. Landragin, Description, modélisation et détection automatique des chaînes de référence (DEMOCRAT), Bulletin de l'AFIA 92, pp.11-15, 2016.

F. Landragin, M. Delaborde, Y. Dupont, and L. Grobol, Description et modélisation des chaînes de référence. Le projet ANR Democrat (2016-2020) et ses avancées à mi-parcours, p.40, 2018.

F. Landragin, T. Poibeau, and B. Victorri, ANALEC: a New Tool for the Dynamic Annotation of Textual Data, Proceedings of the Eighth International Conference on Language Resources and Evaluation. LREC 2012, pp.357-362, 2012.
URL : https://hal.archives-ouvertes.fr/halshs-00698971

E. Lassalle, Structured learning with latent trees: a joint approach to coreference resolution, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01331425

E. Lassalle and P. Denis, Joint Anaphoricity Detection and Coreference Resolution with Constrained Latent Structures, Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI 2015, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01205189

H. Le, FlauBERT: Unsupervised Language Model Pre-training for French, p.101, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02890258

P. Le and I. Titov, Optimizing Differentiable Relaxations of Coreference Evaluation Metrics, 21st Conference on Computational Natural Language Learning, pp.390-399, 2017.

Y. Lecun, A theoretical framework for back-propagation, Proceedings of the 1988 Connectionist Models Summer School, p.80, 1988.

Y. Lecun, L. Bottou, G. B. Orr, and K. Müller, Efficient BackProp, Neural Networks: Tricks of the Trade: Second Edition, p.80, 2012.

H. Lee, A. Chang, Y. Peirsman, N. Chambers, M. Surdeanu et al., Deterministic Coreference Resolution Based on Entity-centric, Precisionranked Rules, Computational Linguistics, vol.39, pp.885-916, 2013.

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 Fifteenth Conference on Computational Natural Language Learning. CoNLL, vol.52, pp.63-65, 2011.

H. Lee, M. Recasens, A. Chang, M. Surdeanu, and D. Jurafsky, Joint Entity and Event Coreference Resolution Across Documents, Proceedings of the 2012, 2012.

, Natural Language Processing and Computational Natural Language Learning. EMNLP-CoNLL, 2012.

H. Lee, M. Surdeanu, and D. Jurafsky, A scaffolding approach to coreference resolution integrating statistical and rule-based models, Natural Language Engineering, vol.23, pp.733-762, 2017.

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, vol.50, pp.78-80, 2017.

K. Lee, L. He, and L. Zettlemoyer, Higher-Order Coreference Resolution with Coarse-to-Fine Inference, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. NAACL: HLT 2018, vol.2, pp.687-692, 2018.

G. ;. Leech and M. Wynne, Developing linguistic corpora: A guide to good practice. AHDS Guides to Good Practice, vol.30, p.29, 2005.

H. Levesque, E. Davis, and L. Morgenstern, The Winograd Schema Challenge, Thirteenth International Conference on Principles of Knowledge Representation and Reasoning, pp.552-561, 2012.

O. Levy, K. Lee, N. Fitzgerald, and L. Zettlemoyer, Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum, p.73, 2018.

Z. Lin, M. Feng, C. Nogueira, M. Santos, B. Yu et al., A Structured Self-Attentive Sentence Embedding, Proceedings of the 5th International Conference on Learning Representations. 5th International Conference on Learning Representations, p.75, 2017.

W. Ling, C. Dyer, A. W. Black, I. Trancoso, R. Fermandez et al., Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.1520-1530, 2015.

F. Liu, L. Zettlemoyer, and J. Eisenstein, The Referential Reader: A Recurrent Entity Network for Anaphora Resolution, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ACL 2019, pp.5918-5925, 2019.

F. Liu, H. Lu, and G. Neubig, Handling Homographs in Neural Machine Translation, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. NAACL-HLT 2018, vol.1, pp.18-1121, 2018.

L. Liu, H. Jiang, P. He, W. Chen, X. Liu et al., On the Variance of the Adaptive Learning Rate and Beyond, p.80, 2019.

Y. Liu, RoBERTa: A Robustly Optimized BERT Pretraining Approach, p.81, 2019.

J. Lonergan, J. Kay, and J. Ross, Étude sociolinguistique sur Orléans : catalogue des enregistrements. Catalog. Orléans, France: Orléans archive, p.37, 1974.

L. Longo, Vers des moteurs de recherche "intelligents" : un outil de détection automatique de thèmes. Méthode basée sur l'identification automatique des chaînes de référence, vol.67, p.65, 2013.

I. Loshchilov and F. Hutter, Decoupled Weight Decay Regularization, 7th International Conference on Learning Representations, vol.82, p.80, 2019.

J. Lu and V. Ng, Event Coreference Resolution: A Survey of Two Decades of Research, International Joint Conferences on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp.5479-5486, 2018.

X. Luo, On Coreference Resolution Performance Metrics, Proceedingss of the 2005 Conference on Human Language Technology and Empirical Methods in Natural Language Processing. HLT '05, pp.25-32, 2005.

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. ACL, vol.63, pp.135-142, 2004.

X. Luo, S. Pradhan, M. Recasens, and E. Hovy, An Extension of BLANC to System Mentions, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. ACL, pp.24-29, 2014.

A. L. Maas, E. Raymond, P. T. Daly, D. Pham, A. Y. Huang et al., Learning Word Vectors for Sentiment Analysis, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. ACL-HLT 2011, p.99, 2011.

E. Manjavacas, Á. Kádár, and M. Kestemont, Improving Lemmatization of Non-Standard Languages with Joint Learning, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. NAACL-HLT 2019, vol.1, pp.1493-1503, 2019.

J. Martens, Second-order Optimization for Neural Networks, vol.179, p.80, 2016.

L. Martin, B. Muller, P. Suárez, Y. Dupont, and L. Romary, CamemBERT: a Tasty French Language Model, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. ACL 2020 (Online), pp.7203-7219, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02445946

S. Martschat, J. Cai, S. Broscheit, É. Mújdricza, -. Maydt et al., A Multigraph Model for Coreference Resolution, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, p.59, 2012.

Y. Mathet and A. Widlöcher, Stratégie d'exploration de corpus multi-annotés avec GlozzQL, Actes de la 18e Conférence Traitement Automatique des Langues Naturelles. TALN 2011, vol.1, p.47, 2011.

A. Mccallum and B. Wellner, Conditional Models of Identity Uncertainty with Application to Noun Coreference, Advances in Neural Information Processing Systems. 17th International Conference on Neural Information Processing Systems, vol.17, pp.905-912, 2004.

J. F. Mccarthy and W. G. Lehnert, Using decision trees for conference resolution, Proceedings of the 14th international joint conference on Artificial intelligence. IJCAI 1995, vol.2, pp.1050-1055, 1995.

R. Mccoy, E. Thomas, T. Pavlick, and . Linzen, Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference, 2019.

A. Meechan-maddon and J. Nivre, How to Parse Low-Resource Languages: Cross-Lingual Parsing, Target Language Annotation, or Both?, ' In: Proceedings of the Fifth International Conference on Dependency Linguistics. Depling, SyntaxFest, pp.112-120, 2019.

F. Mélanie-becquet and F. Landragin, Linguistique outillée pour l'étude des chaînes de référence questions méthodologiques et solutions techniques, Langages 195, p.41, 2014.

T. Mikolov, Statistical Language Models Based on Neural Networks, vol.133, p.99, 2012.

T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space, 1st International Conference on Learning Representations, 2013.

T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, and A. Joulin, Advances in Pre-Training Distributed Word Representations, Proceedings of the 11th International Conference on Language Resources and Evaluation. LREC, 2018.

, European Language Resource Association, vol.100, p.99

G. A. Miller, WordNet: a lexical database for English, Communications of the ACM, vol.38, p.51, 1995.

Y. Miyamoto and K. Cho, Gated Word-Character Recurrent Language Model, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, p.79, 2016.

N. Moosavi, L. Sadat, M. Born, M. Poesio, and . Strube, Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ACL 2019, pp.4168-4178, 2019.

N. Moosavi, M. Sadat, and . Strube, Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. ACL 2016, vol.1, pp.632-642, 2016.

P. Morerio, J. Cavazza, R. Volpi, R. Vidal, and V. Murino, Curriculum Dropout, p.81, 2017.

. Muc-consortium, Appendix C: Named Entity Task Definition (v2.1)'. In: Sixth Message Understanding Conference, p.16, 1995.

. Muc-consortium, Appendix D: Coreference Task Definition (v2.3)'. In: Sixth Message Understanding Conference, 1995.

. Muc-consortium, Proceedings of a Conference Held in Columbia, 1995.

. Muc-consortium, Seventh Message Understanding Conference, 1998.

J. Munkres, Algorithms for the Assignment and Transportation Problems, Journal of the Society for Industrial and Applied Mathematics, vol.1, issue.5, p.24, 1957.

J. Muzerelle, A. Lefeuvre, J. Antoine, E. Schang, D. Maurel et al., ANCOR, premier corpus de français parlé d'envergure annoté en coréférence et distribué librement, Actes de la 20ème conférence sur le Traitement Automatique des Langues Naturelles. TALN'2013. Les Sable d'Olonne, France: Association pour le Traitement Automatique des Langues, pp.555-563, 2013.

J. Muzerelle, A. Lefeuvre, E. Schang, J. Antoine, A. Pelletier et al., ANCOR Centre, a Large Free Spoken French Coreference Corpus: Description of the Resource and Reliability Measures, Proceedings of the 9th International Conference on Language Resources and Evaluation. LREC, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01075679

J. Muzerelle, E. Schang, and J. Antoine, Annotation en relations anaphoriques d'un corpus de discours oral spontané en français, Congrès Mondial de Linguistique Française. CMLF'2012, 2013.

V. Nair and G. E. Hinton, Rectified Linear Units Improve Restricted Boltzmann Machines, 27th International Conference on International Conference on Machine Learning, pp.807-814, 2010.

/. , org/citation.cfm?id=3104322.3104425 (visited on 30/09/2019) (cit, p.88

A. Nasr, F. Béchet, J. Rey, B. Favre, and J. L. Roux, MACAON : An NLP Tool Suite for Processing Word Lattices, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. ACL 2011. United States, p.65, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01194259

A. Nedoluzhko, M. Novák, S. Cinkova, M. Mikulová, and J. Mírovský, Coreference in Prague Czech-English Dependency Treebank, Proceedings of the Tenth International Conference on Language Resources and Evaluation. LREC 2016, vol.40, p.32, 2016.

V. Ng, Unsupervised Models for Coreference Resolution, Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp.640-649, 2008.

V. Ng and C. Cardie, Improving Machine Learning Approaches to Coreference Resolution, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. ACL 2002, vol.65, p.61, 2002.

P. Nicolas, S. Letellier-zarshenas, I. Schadle, J. -. , Y. Antoine et al., Towards a large corpus of spoken dialogue in French that will be freely available: the "Parole Publique" project and its first realisations, Proceedings of the Third International Conference on Language Resources and Evaluation. LREC 2002, p.37, 2002.

B. Nito?, P. Morawiecki, and M. Ogrodniczuk, Deep Neural Networks for Coreference Resolution for Polish, Proceedings of the Eleventh International Conference on Language Resources and Evaluation. LREC 2018. Miyazaki, Japan: European Languages Resources Association, 2018.

T. Niven and H. Kao, Probing Neural Network Comprehension of Natural Language Arguments, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ACL 2019, pp.19-1459, 2019.

J. Nivre, Universal Dependencies v1: A Multilingual Treebank Collection, Proceedings of the Tenth International Conference on Language Resources and Evaluation. LREC 2016, vol.103, p.45, 2016.

J. Nothman, N. Ringland, W. Radford, T. Murphy, and J. R. Curran, Learning multilingual named entity recognition from Wikipedia, Artificial Intelligence. Artificial Intelligence, Wikipedia and Semi-Structured Resources 194, p.103, 2013.

M. Ogrodniczuk, K. G?owi?ska, M. Kope?, A. Savary, and M. Zawis?awska, Lecture Notes in Computer Science, Human Language Technology. Challenges for Computer Science and Linguistics, pp.215-226, 2016.

, Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes, 2016.

, Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes, 2017.

O. Suárez, P. Javier, Y. Dupont, B. Muller, L. Romary et al., Establishing a New State-of-the-Art for Named Entity Recognition, Proceedings of the 11th International Conference on Language Resources and Evaluation. LREC 2020, p.101, 2020.

O. Suárez, P. Javier, B. Sagot, and L. Romary, Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures'. In: 7th Workshop on the Challenges in the Management of Large Corpora. CMLCè7, 2019.

E. By-piotr, A. Ba?ski, H. Barbaresi, E. Biber, S. Breiteneder et al., Leibniz-Institut für Deutsche Sprache, 2019.

, A???dhy?y? (cit, p.106

A. Paszke, Automatic differentiation in PyTorch, NeurIPS 2017 Autodiff Workshop: The Future of Gradient-based Machine Learning Software and Techniques, p.80, 2017.

J. Pennington, R. Socher, and C. Manning, Glove: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp.1532-1543, 2014.

M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark et al., Deep Contextualized Word Representations, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. NAACL: HLT 2018, vol.1, pp.2227-2237, 2018.

B. Plank, A. Søgaard, and Y. Goldberg, Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. ACL 2016, vol.2, p.71, 2016.

M. Poesio, The MATE/GNOME Proposals for Anaphoric Annotation, Revisited, Proceedings of the 5th SIGDIAL Workshop, p.40, 2004.

M. Poesio, Linguistic and Cognitive Evidence About Anaphora, Anaphora Resolution: Algorithms, Resources, and Applications. Ed. by Massimo Poesio, Roland Stuckardt and Yannick Versley. Theory and Applications of Natural Language Processing, pp.23-54, 2016.

M. Poesio, M. Alexandrov-kabadjov, R. Vieira, R. Goulart, and O. Uryupina, Does discourse-new detection help definite description resolution, Proceedings of the 6th International Workshop on Computational Semantics. IWCS 2005, pp.236-246, 2005.

M. Poesio and R. Artstein, Anaphoric Annotation in the ARRAU Corpus, Proceedings of the 10th International Conference on Language Resources and Evaluation. LREC, 2008.

M. Poesio, F. Bruneseaux, and L. Romary, The MATE meta-scheme for coreference in dialogues in multiple languages, ACL'99 Workshop Towards Standards and Tools for Discourse Tagging. College Parc, p.40, 1999.
URL : https://hal.archives-ouvertes.fr/inria-00525171

M. Poesio, S. Pradhan, M. Recasens, K. Rodriguez, and Y. Versley, Annotated Corpora and Annotation Tools, Anaphora Resolution: Algorithms, Resources, and Applications. Ed. by Massimo Poesio, Roland Stuckardt and Yannick Versley. Theory and Applications of Natural Language Processing, pp.97-163, 2016.

M. Poesio, Anaphora Resolution: Algorithms, Resources, and Applications. Theory and Applications of Natural Language Processing, 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.17, 2016.

M. Poesio, Anaphora Resolution with the ARRAU Corpus, Proceedings of the First Workshop on Computational Models of Reference, pp.11-22, 2018.

A. Popescu-belis, Modélisation multi-agents des échanges langagiers : application au problème de la référence et à son évaluation'. thesis. Paris 11, vol.36, p.35, 1999.

A. Popescu-belis, L. Rigouste, S. Salmon-alt, and L. Romary, Online Evaluation of Coreference Resolution, 4th International Conference on Language Resources and Evaluation. LREC, 2004.
URL : https://hal.archives-ouvertes.fr/halshs-00005023

S. Pradhan, E. Hovy, M. Marcus, M. Palmer, L. Ramshaw et al., OntoNotes: A Unified Relational Semantic Representation, Proceedings of the First IEEE International Conference on Semantic Computing. ICSC, pp.517-526, 2007.

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. ACL 2014, vol.2, pp.30-35, 2014.

S. Pradhan, A. Moschitti, N. Xue, O. Uryupina, and Y. Zhang, CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes, Proceedings of the Joint EMNLP-CoNLL conference, pp.1-40, 2012.

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

S. Pradhan, L. Ramshaw, R. Weischedel, J. Macbride, and L. Micciulla, Unrestricted Coreference: Identifying Entities and Events in OntoNotes, Proceedings of the First IEEE International Conference on Semantic Computing. ICSC, pp.446-453, 2007.

E. Prince and . Friedman, Toward a taxonomy of given -new information'. In: Radical pragmatics, vol.40, p.12, 1981.

M. Quignard, S. Heiden, F. Landragin, and M. Decorde, Textometric Exploitation of Coreference-annotated Corpora with TXM: Methodological Choices and First Outcomes, Proceedings of the Fourteenth International Conference on the Statistical Analysis of Textual Data. JADT 2018. UniversItalia, 11th, p.47, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01814858

A. Radford, J. Wu, R. Child, D. Luan, D. Amodei et al., Language models are unsupervised multitask learners' (cit, p.81, 2019.

K. Raghunathan, H. Lee, S. Rangarajan, N. Chambers, M. Surdeanu et al., A Multi-pass Sieve for Coreference Resolution, Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. EMNLP 2010, pp.492-501, 2010.

A. Rahman and V. Ng, Supervised models for coreference resolution, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol.2, p.85, 2009.

A. Rahman and V. Ng, Narrowing the Modeling Gap: A Cluster-Ranking Approach to Coreference Resolution, Journal of Artificial Intelligence Research, vol.40, p.63, 2011.

W. M. Rand, Objective Criteria for the Evaluation of Clustering Methods', Journal of the American Statistical Association, vol.66, p.25, 1971.

F. Rastier, Enjeux épistémologiques de la linguistique de corpus'. In: Deuxièmes journées de la linguistique de corpus, p.30, 2002.

M. Recasens, Coreference: Theory, Annotation, Resolution and Evaluation, 2010.

M. Recasens, M. De-marneffe, and C. Potts, The Life and Death of Discourse Entities: Identifying Singleton Mentions, Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. NAACL: HLT 2013, pp.627-633, 2013.

M. Recasens and E. Hovy, A Deeper Look into Features for Coreference Resolution, Anaphora Processing and Applications, p.52, 2009.

M. Recasens and E. Hovy, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. ACL 2010, vol.63, p.17, 2010.

M. Recasens and E. Hovy, BLANC: Implementing the Rand Index for Coreference Evaluation, Natural Language Engineering, vol.17, pp.23-25, 2011.

M. Recasens, E. Hovy, and M. Martí, Identity, non-identity, and nearidentity: Addressing the complexity of coreference, Lingua, vol.121, issue.6, pp.1138-1152, 2011.

M. Recasens, L. Màrquez, E. Sapena, M. Martí, M. Taulé et al., SemEval-2010 task 1: Coreference resolution in multiple languages, Proceedings of the 5th International Workshop on Semantic Evaluation. SemEval, pp.1-8, 2010.

M. Recasens, L. Màrquez, E. Sapena, M. Martí, M. Taulé et al., SemEval-2010 task 1: Coreference resolution in multiple languages. Dataset release LDC2011T01, p.35, 2011.

S. J. Reddi, S. Kale, and S. Kumar, On the Convergence of Adam and Beyond, Sixth International Conference on Learning Representations, p.80, 2018.

K. Rodríguez, F. Joseba, Y. Delogu, . Versley, W. Egon et al., Anaphoric Annotation of Wikipedia and Blogs in the Live Memories Corpus, Proceedings of the Seventh International Conference on Language Resources and Evaluation. LREC 2010, p.40, 2010.

I. Roesiger, M. Köper, K. , A. Nguyen, and S. Schulte-im-walde, Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution, Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference, p.77, 2018.

A. Rogers, How the Transformers broke NLP leaderboards, p.73, 2019.

L. Romary, stdfSpec : A proposal for a stand-off element for the TEI Guidelines, p.44, 2017.

S. Rosset, C. Grouin, and P. Zweigenbaum, Entités nommées structurées : guide d'annotation Quaero. Notes LIMSI 2011-04. Orsay, France: Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur, p.86, 2011.

D. Roth and W. Yih, Global Inference for Entity and Relation Identification via a Linear Programming Formulation'. In: Introduction to Statistical Relational Learning, p.61, 2007.

H. Rubenstein and J. B. Goodenough, Contextual correlates of synonymy, Communications of the ACM, vol.8, issue.10, p.99, 1965.

S. Ruder, An Overview of Multi-Task Learning in Deep Neural Networks, p.86, 2017.

S. Ruder, Neural Transfer Learning for Natural Language Processing, p.85, 2019.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol.323, p.80, 1986.

B. Russell, The philosophy of logical atomism, The Monist 29.1, p.9, 1919.

K. Sakaguchi, R. Le-bras, C. Bhagavatula, and Y. Choi, WINO-GRANDE: An Adversarial Winograd Schema Challenge at Scale, 2019.

T. Salimans and D. P. Kingma, Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks, p.82, 2016.

S. Salmon-alt, Le projet ANANAS : Annotation anaphorique pour l'analyse sémantique de corpus, Actes de la 9ème conférence annuelle sur le Traitement Automatique des Langues Naturelles. TALN 2002, vol.36, p.35, 2002.

S. Salmon-alt, E. Bick, L. Romary, and J. Pierrel, La FReeBank : vers une base libre de corpus annotés, Traitement Automatique des Langues Naturelles -TALN'04, vol.10, p.35, 2004.

V. Sanh, T. Wolf, and S. Ruder, A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks, 2018.

S. Santurkar, D. Tsipras, A. Ilyas, and A. Madry, How Does Batch Normalization Help Optimization?, ' In: (29th May, p.83, 2018.

H. Schmid, Probabilistic Part-of-Speech Tagging Using Decision Trees, Proceedings of the International Conference on New Methods in Language Processing. International Conference on New Methods in Language Processing, p.40, 1994.

M. Schuster and K. K. Paliwal, Bidirectional recurrent neural networks', IEEE Transactions on Signal Processing, vol.45, pp.2673-2681, 1997.

S. Sekine and C. Nobata, Definition, Dictionaries and Tagger for Extended Named Entity Hierarchy, Proceedings of the Fourth International Conference on Language Resources and Evaluation. LREC, 2004.

R. Sennrich, B. Haddow, and A. Birch, Neural Machine Translation of Rare Words with Subword Units, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. ACL 2016, p.102, 2016.

F. Sha and F. Pereira, Shallow Parsing with Conditional Random Fields, Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. HLT-NAACL 2003, p.105, 2003.

D. Shen, Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. ACL 2018, vol.1, pp.440-450, 2018.

H. Shimodaira, Improving predictive inference under covariate shift by weighting the log-likelihood function, Journal of Statistical Planning and Inference, vol.90, issue.2, pp.227-244, 2000.

M. Shoeybi, M. Patwary, R. Puri, P. Legresley, J. Casper et al., Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism'. In: (17th Sept. 2019), p.81, 2019.

G. Simpson and . Gaylord, Holarctic Mammalian Faunas and Continental Relationships during the Cenozoic, Geological Society of America Bulletin, vol.58, p.58, 1947.

A. Smith, B. Bohnet, J. Miryam-de-lhoneux, Y. Nivre, S. Shao et al., 82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models, Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. CoNLL, pp.113-123, 2018.

R. Smyth, Grammatical determinants of ambiguous pronoun resolution, Journal of Psycholinguistic Research, vol.23, issue.3, p.56, 1994.

A. Søgaard and Y. Goldberg, Deep multi-task learning with low level tasks supervised at lower layers, 54th Annual Meeting of the Association for Computational Linguistics. ACL 2016, vol.2, pp.231-235, 2016.

Z. Song, From Light to Rich ERE: Annotation of Entities, Relations, and Events'. In: 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation, pp.89-98, 2015.

W. Soon, . Meng, T. Hwee, C. Y. Ng, and . Lim, A Machine Learning Approach to Coreference Resolution of Noun Phrases, Computational Linguistics 27, vol.4, p.65, 2001.

A. Soraluze, O. Arregi, X. Arregi, K. Ceberio, and A. Díaz-de-ilarraza, Mention detection: First steps in the development of a Basque coreference resolution system, Proceedings of KONVENS 2012, pp.128-136, 2012.

A. Soraluze, O. Arregi, X. Arregi, and A. Díaz-de-ilarraza, EUSKOR: End-to-end coreference resolution system for Basque, PLOS ONE, vol.14, issue.9, p.53, 2019.

A. Soraluze, O. Arregi, X. Arregi, and A. Ilarraza, Improving mention detection for Basque based on a deep error analysis, Natural Language Engineering, vol.23, issue.3, pp.351-384, 2017.

T. J. Sørensen, A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons, p.24, 1948.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, vol.15, p.81, 2014.

M. Stern, J. Andreas, and D. Klein, A Minimal Span-Based Neural Constituency Parser, 55th Annual Meeting of the Association for Computational Linguistics, vol.1, pp.17-1076, 2017.

R. J. Stevenson, W. R. Alexander, K. Nelson, and . Stenning, The Role of Parallelism in Strategies of Pronoun Comprehension, Language and Speech, vol.38, issue.4, p.56, 1995.

. Stoyanov, C. Veselin, N. Cardie, E. Gilbert, D. Riloff et al., Coreference Resolution with Reconcile, Proceedings of the ACL 2010 Conference. ACL 2010, p.65, 2010.

V. Stoyanov and J. Eisner, Easy-first Coreference Resolution, p.64, 2012.

. Stoyanov, N. Veselin, C. Gilbert, E. Cardie, and . Riloff, 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 ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. ACL-AFNLP 2009, 2009.

M. Straka, J. Straková, ;. Tokenizing, and . Tagging, Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Proceedings of the CoNLL, p.45, 2017.

M. Straka and J. Straková, ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task, Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning. CoNLL, pp.127-137, 2019.

J. Straková, M. Straka, and J. Hajic, Neural Architectures for Nested NER through Linearization, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. ACL 2019, pp.5326-5331, 2019.

P. Sturt and V. Lombardo, Processing Coordinated Structures: Incrementality and Connectedness, Cognitive Science, vol.29, p.58, 2005.

S. Sukhbaatar, A. Szlam, J. Weston, R. Fergus, ;. C. Cortes et al., End-To-End Memory Networks, Advances in Neural Information Processing Systems. NeurIPS, vol.28, p.57, 2015.

I. Sutskever, O. Vinyals, ;. Z. Quoc-v-le, M. Ghahramani, C. Welling et al., Sequence to Sequence Learning with Neural Networks, Advances in Neural Information Processing Systems. 28th conference on Neural Information Processing, vol.27, pp.3104-3112, 2014.

S. Swayamdipta, S. Thomson, K. Lee, L. Zettlemoyer, C. Dyer et al., Syntactic Scaffolds for Semantic Structures, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp.3772-3782, 2018.

D. Szymkiewicz, Une Contribution statistique à la géographie floristique, Polskie Towarzystwo Botaniczne. 17 pp. Google Books: AK13oAEACAAJ (cit, p.98, 1934.

M. Taulé, M. , A. Martí, and M. Recasens, AnCora: Multilevel Annotated Corpora for Catalan and Spanish, Proceedings of the 6th International Conference on Language Resources and Evaluation. Marrakech, Morroco: European Language Resources Association, 2008.

, TEI consortium (2020). TEI P5: Guidelines for Electronic Text Encoding and Interchange, 2020.

I. Tellier, Y. Dupont, and A. Courmet, Un segmenteur-étiqueteur et un chunker pour le français, Actes de la conférence conjointe JEP-TALN-RECITAL 2012. TALN 2012, vol.5, p.106, 2012.

I. Tellier, Y. Dupont, I. Eshkol, and I. Wang, Adapt a Text-Oriented Chunker for Oral Data: How Much Manual Effort Is Necessary?, ' In: Proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning. IDEAL 2013, Special Session on Text Data Learning (??, ?? (Héféi, China)). Ed. by Hujun Yin, p.106, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01174605

I. Tellier, I. Eshkol-taravella, Y. Dupont, and I. Wang, Peut-on bien chunker avec de mauvaises étiquettes POS ?' In: Actes de la 21ème conférence sur le Traitement Automatique des Langues Naturelles, Association pour le Traitement Automatique des Langues, pp.125-136, 2014.

H. Telljohann, E. W. Hinrichs, S. Kübler, H. Zinsmeister, and K. Beck, Stylebook for the Tübingen treebank of written German (TüBa-D/Z), 2006.

F. Trouilleux, Referential links identification and automatic interpretation of pronominal expressions in French texts, vol.67, 2001.
URL : https://hal.archives-ouvertes.fr/tel-01152394

J. Turian, L. -. , A. Ratinov, and Y. Bengio, Word Representations: A Simple and General Method for Semi-Supervised Learning, Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. ACL 2010, p.99, 2010.

A. Tutin, F. Trouilleux, C. Clouzot, É. Gaussier, A. Zaenen et al., Annotating a large corpus with anaphoric links, Proceedings of the 3rd International Conference on Discourse Anaphora and Anaphora Resolution. DAARC, vol.36, p.35, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00373327

O. Uryupina, High-precision Identification of Discourse New and Unique Noun Phrases, The Companion Volume to the Proceedings of 41st Annual Meeting of the Association for Computational Linguistics. ACL 2003, pp.3-2012, 2003.

O. Uryupina, Evaluating Name-Matching for Coreference Resolution, Proceedings of the Fourth International Conference on Language Resources and Evaluation. LREC, p.52, 2004.

O. Uryupina, Coreference Resolution with and without Linguistic Knowledge, Proceedings of the Fifth International Conference on Language Resources and Evaluation. LREC, p.52, 2006.

O. Uryupina, Knowledge acquisition for coreference resolution, vol.280, p.52, 2007.

O. Uryupina, Corry: A System for Coreference Resolution, Proceedings of the 5th International Workshop on Semantic Evaluation. SemEval, pp.100-103, 2010.

O. Uryupina, R. Artstein, A. Bristot, F. Cavicchio, F. Delogu et al., Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU Corpus, Natural Language Engineering, pp.1-34, 2019.

O. Uryupina, M. Kabadjov, and M. Poesio, Detecting Non-reference and Nonanaphoricity, Anaphora Resolution: Algorithms, Resources, and Applications. Ed. by Massimo Poesio, Roland Stuckardt and Yannick Versley. Theory and Applications of Natural Language Processing, pp.23-54, 2016.

O. Uryupina and A. Moschitti, Multilingual Mention Detection for Coreference Resolution, Proceedings of the sixth International Joint Conference on Natural Language Processing. IJCNLP 2013, pp.100-108, 2013.

. Van-deemter, R. Kees, and . Kibble, On Coreferring: Coreference in MUC and Related Annotation Schemes, Computational Linguistics 26, vol.4, p.32, 2000.

C. Van-rijsbergen and . Joost, Information Retrieval, p.20, 1979.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Attention is All you Need'. In: Advances in Neural Information Processing Systems 30, vol.101, pp.5998-6008, 2017.

Y. Versley, S. P. Ponzetto, M. Poesio, V. Eidelman, A. Jern et al., BART: A Modular Toolkit for Coreference Resolution, Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session. ACL-08: HLT, p.51, 2008.

R. Vieira and M. Poesio, An Empirically-based System for Processing Definite Descriptions, Computational Linguistics 26, vol.4, pp.539-593, 2000.

M. Vilain, J. Burger, J. Aberdeen, D. Connolly, and L. Hirschman, A Model-Theoretic Coreference Scoring Scheme, Sixth Message Understanding Conference, pp.45-52, 1995.

L. Villemonte-de, . Clergerie, B. Éric, D. Sagot, and . Seddah, The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. CoNLL, vol.107, p.45, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01584168

O. Vinyals, L. Kaiser, T. Koo, S. Petrov, I. Sutskever et al., Grammar As a Foreign Language, Proceedings of the 28th International Conference on Neural Information Processing, vol.2, pp.2773-2781, 2015.

L. Wan, M. Zeiler, S. Zhang, Y. Le-cun, and R. Fergus, Regularization of Neural Networks using DropConnect, International Conference on Machine Learning. International Conference on Machine Learning. 13th Feb, p.81, 2013.

A. Wang, Y. Pruksachatkun, N. Nangia, A. Singh, J. Michael et al., SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems, 2019.

A. Wang, A. Singh, J. Michael, F. Hill, O. Levy et al., GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding, Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. BlackboxNLP, pp.353-355, 2018.

B. Wang, W. Lu, Y. Wang, and H. Jin, A Neural Transition-based Model for Nested Mention Recognition, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp.1011-1017, 2018.

W. Wang and B. Chang, Graph-based Dependency Parsing with Bidirectional LSTM, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. ACL 2016, vol.1, pp.2306-2315, 2016.

. Warde-farley, I. J. David, A. Goodfellow, Y. Courville, and . Bengio, An empirical analysis of dropout in piecewise linear networks, p.81, 2013.

B. Webber and . Lynn, Description Formation and Discourse Model Synthesis'. In: Theoretical Issues in Natural Language Processing-2, 1978.

B. Webber and . Lynn, Structure and ostension in the interpretation of discourse deixis, Language and Cognitive Processes, vol.6, pp.107-135, 1991.

F. Weber and S. Beaud, Guide de l'enquête de terrain : produire et analyser des données ethnographiques. Paris: Éditions la Découverte, vol.356, p.37, 2003.

K. Webster, M. Recasens, V. Axelrod, and J. Baldridge, Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns, Transactions of the Association for Computational Linguistics, vol.6, pp.605-617, 2018.

R. Weischedel, OntoNotes release 5.0 with OntoNotes DB Tool v0.999 beta. Dataset release LDC2013T19. Philadelphia: Linguistic Data Consortium, vol.34, p.33, 2013.

G. Weiss, Y. Goldberg, and E. Yahav, On the Practical Computational Power of Finite Precision RNNs for Language Recognition, 56th Annual Meeting of the Association for Computational Linguistics, vol.2, pp.740-745, 2018.

L. Weng, Are Deep Neural Networks Dramatically Overfitted?, p.81, 2019.

J. Weston, S. Chopra, and A. Bordes, Memory Networks, Proceedings of the 3rd International Conference on Learning Representations. ICLR 2015, p.57, 2015.

A. Widlöcher, Analyse macro-sémantique des structures rhétoriques du discours : cadre théorique et modèle opératoire'. thesis. Caen, p.41, 2008.

A. Widlöcher and Y. Mathet, The Glozz Platform: A Corpus Annotation and Mining Tool', 2012 ACM Symposium on Document Engineering. DocEng '12, vol.42, p.41, 2012.

T. Winograd, Understanding natural language, Cognitive Psychology, vol.3, pp.1-191, 1972.

. 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. NAACL: HLT 2016, pp.994-1004, 2016.

. Wiseman, A. M. Sam, . Rush, M. Stuart, J. Shieber et al., Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. ACL-IJCNLP 2015 (???, ??, vol.1, pp.1416-1426, 2015.

C. Wong, N. Houlsby, Y. Lu, A. Gesmundo, ;. S. Bengio et al., Transfer Learning with Neural AutoML, Advances in Neural Information Processing Systems 31. 2018 conference on Neural Information Processing Systems, p.93, 2018.

X. Yang, J. Su, G. Zhou, and C. Lim-tan, An NP-cluster Based Approach to Coreference Resolution, Proceedings of the 20th International Conference on Computational Linguistics. ACL, vol.63, p.57, 2004.

X. Yang, G. Zhou, J. Su, and C. Lim-tan, Coreference Resolution Using Competition Learning Approach, Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics. ACL 2003, p.59, 2003.

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, Understanding deep learning requires rethinking generalization, p.81, 2016.

C. Zhang, S. Bengio, and Y. Singer, Are All Layers Created Equal?, ' In: Proceedings of the workshop on Identifying and Understanding Deep Learning Phenomena. 36th International Conference on Machine Learning, p.93, 2019.

R. Zhang, C. Nogueira, M. Santos, B. Yasunaga, D. Xiang et al., Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering, 56th Annual Meeting of the Association for Computational Linguistics. ACL 2018, vol.2, pp.102-107, 2018.

A. Zidouni, S. Rosset, and H. Glotin, Efficient combined approach for named entity recognition in spoken language, Proceedings of the 11th Annual Conference of the International Speech Communication Association. InterSpeech, p.106, 2010.

G. Zipf and . Kingsley, Human behavior and the principle of least effort: An introduction to human ecology, p.70, 1949.

B. Zoph and Q. V. Le, Neural Architecture Search with Reinforcement Learning, 5th International Conference on Learning Representations, p.93, 2017.

, List of Figures 3.1 Syntactic analysis (subtree) for "au moment où je me suis marié en juillet soixantesept"

, DeCOFre inference mode operating process

, Contextual words representations in DeCOFre

. Boundaries and . .. Decofre, , p.74

, Soft-head self-attention in DeCOFre

, List of Tables 2.1 Definition of the BLANC metric

, French corpora with anaphora annotations prior, p.36, 2002.

. .. , ANCOR subcorpora dimensions (from Muzerelle et al. 2014), p.37

. .. , Embedding dimensions in DeCOFre baseline, vol.87

. .. , 2 Feedfoward networks parameters in DeCOFre baseline, vol.88

, Recurrent layers parameters in DeCOFre baseline

, Mention detection evaluation (ANCOR test)

. .. , Coreference resolution evaluation (ANCOR test), p.90

, Influence of training setting on mention detection

. .. , Influence of the training setting on coreference resolution, p.91

, Influence of the learning rate schedule on mention detection, p.92

. .. , 92 5.10 Influence of undersampling rate on mention detection perfomances, p.93

. .. , Influence of the length feature on mention detection (ANCOR dev), p.97

, 2 Influence of the length feature on coreference resolution (ANCOR dev), p.97

, Influence of string matching features on coreference resolution, p.99

. .. , Influence of word embeddings on mention detection (ANCOR dev), vol.102

, Influence of shallow linguistic knowledge on mention detection (ANCOR dev) 104

, Influence of shallow linguistic knowledge on coreference resolution (ANCOR dev) 104

, Influence of structural linguistic knowledge on mention detection, p.108

, Influence of structural linguistic knowledge on coreference resolution (ANCOR dev)