H. Adel, B. Roth, H. Schütze-;-airoldi, E. , D. Blei et al., Comparing Convolutional Neural Networks to Traditional Models for Slot Filling, Proceedings of the 3rd International Workshop on Link Discovery. LinkKDD '05, pp.82-89, 2005.

Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid, Label-Embedding for Image Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.7, pp.1425-1438, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01207145

J. Allan, Relevance feedback with too much data, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '95, pp.337-343, 1995.

M. R. Amini and N. Usunier, Transductive learning over automatically detected themes for multi-document summarization, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01286161

G. Andrew, R. Arora, J. A. Bilmes, and K. Livescu, Deep Canonical Correlation Analysis, Proceedings of the 30th International Conference on Machine Learning, ICML 2013, pp.1247-1255, 2013.

R. Angelova, G. Kasneci, and G. Weikum, Graffiti: graph-based classification in heterogeneous networks, World Wide Web, vol.15, issue.2, pp.139-170, 2011.

R. Angelova, G. Kasneci, and G. Weikum, Graffiti: graph-based classification in heterogeneous networks, World Wide Web, vol.15, issue.2, pp.139-170, 2011.

M. Arjovsky, A. Shah, and Y. Bengio, Unitary Evolution Recurrent Neural Networks, 2015.

S. Arora, Y. Li, Y. Liang, T. Ma, and A. Risteski, A Latent Variable Model Approach to PMI-based Word Embeddings, Transactions of the Association for Computational Linguistics, vol.4, pp.385-399, 2016.

I. Arroyo-fernández, C. Méndez-cruz, G. Sierra, J. Torres-moreno, and G. Sidorov, Unsupervised sentence representations as word information series: Revisiting TF?IDF, Computer Speech & Language, vol.56, pp.107-129, 2019.

H. Bagherinezhad, H. Hajishirzi, Y. Choi, and A. Farhadi, Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp.3449-3456, 2016.

M. Baroni, G. Dinu, and G. Kruszewski, Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014.

R. Barzilay and M. Elhadad, Using Lexical Chains for Text Summarization, In Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, pp.10-17, 1997.

H. Bay, A. Ess, T. Tuytelaars, and L. Van-gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008.

L. Beinborn, T. Botschen, and I. Gurevych, Multimodal Grounding for Language Processing, 2018.

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, pp.711-720, 1997.

N. J. Belkin, R. N. Oddy, and H. M. Brooks, ASK FOR INFORMATION RETRIEVAL: PART I. BACKGROUND AND THEORY, Journal of Documentation, vol.38, issue.2, pp.61-71, 1982.

I. Beltagy, K. Lo, and A. Cohan, SciBERT: A Pretrained Language Model for Scientific Text, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp.3606-3611, 2019.

I. Beltagy, M. E. Peters, and A. Cohan, 10.3726/978-3-653-05150-6/10, Inactive DOIs

E. M. Bender and A. Koller, Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, p.13, 2020.

Y. Bengio, Continuous optimization of hyper-parameters, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, vol.1, pp.305-310, 2000.

Y. Bengio, A. Courville, and P. Vincent, Representation Learning: A Review and New Perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013.

Y. Bengio, A. Courville, and P. Vincent, Representation Learning: A Review and New Perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013.

Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, A Neural Probabilistic Language Model, In: The Journal of Machine Learning Research, pp.1137-1155, 2003.

T. R. Besold, A. D. Garcez, S. Bader, H. Bowman, P. Domingos et al., Neural-Symbolic Learning and Reasoning: A Survey and Interpretation, 2017.

E. D. Bezenac, A. Pajot, and P. Gallinari, Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge, International Conference on Learning Representations, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02418362

C. Bishop, Pattern Recognition and Machine Learning, 2006.

D. Blei, L. Carin, and D. Dunson, Probabilistic Topic Models, IEEE Signal Processing Magazine, 2010.

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.

A. Bojchevski and S. Günnemann, Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking, 2017.

, 10.3726/978-3-653-03815-6/4, Inactive DOIs

A. Bordes, R. Collobert, J. Weston, and Y. Bengio, Learning Structured Embeddings of Knowledge Bases, Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752498

A. Bordes, N. Usunier, A. Garcia-duran, J. Weston, and O. Yakhnenko, Translating Embeddings for Modeling Multi-relational Data, pp.2787-2795, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00920777

J. Bos, V. Basile, K. Evang, N. J. Venhuizen, and J. Bjerva, The Groningen Meaning Bank, Handbook of Linguistic Annotation, pp.463-496, 2017.

L. Bottou, J. Peters, J. Quiñonero-candela, D. X. Charles, D. M. Chickering et al., Counterfactual Reasoning and Learning Systems, 2013.

J. Bradbury and R. Socher, Towards Neural Machine Translation with Latent Tree Attention, Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing, 2017.

D. Britz, A. Goldie, M. Luong, and Q. Le, Massive Exploration of Neural Machine Translation Architectures, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017.

P. Broek, M. Young, Y. Tzeng, T. Linderholm, and H. V. Oostendorp, The landscape model of reading, 1999.

G. G. Brown and H. C. Rutemiller, Means and Variances of Stochastic Vector Products with Applications to Random Linear Models, Management Science, vol.24, issue.2, 1977.

E. Bruni, G. Boleda, M. Baroni, and N. Tran, Concreteness ratings for 40 thousand generally known English word lemmas, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics 1.July, vol.46, pp.904-911, 2012.

M. Bucher, S. Herbin, and F. Jurie, Generating Visual Representations for Zero-Shot Classification, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01576222

C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds et al., Learning to rank using gradient descent, Proceedings of the 22nd international conference on Machine learning - ICML '05, pp.89-96, 2005.

C. Burgess, K. Livesay, and K. Lund, Explorations in context space: Words, sentences, discourse, Discourse Processes, vol.25, issue.2-3, pp.211-257, 1998.

C. Burgess and K. Lund, Modelling parsing constraints with high-dimensional context space, p.12, 1997.

S. Cao, W. Lu, and Q. Xu, GraRep, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15, pp.891-900, 2015.

A. Caputo, B. Piwowarski, and M. Lalmas, A Query Algebra for Quantum Information Retrieval, Proceedings of the 2nd Italian Information Retrieval Workshop, 2011.

J. Carbonell and J. Goldstein, The use of MMR, diversity-based reranking for reordering documents and producing summaries, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '98, 1998.

M. Carvalho, R. Cadène, D. Picard, L. Soulier, N. Thome et al., Cross-Modal Retrieval in the Cooking Context, The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp.35-44, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01839068

L. Castrejon, Y. Aytar, C. Vondrick, H. Pirsiavash, and A. Torralba, Learning Aligned Cross-Modal Representations from Weakly Aligned Data, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

D. Cer, Y. Yang, S. Kong, N. Hua, N. Limtiaco et al., Universal Sentence Encoder for English, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, vol.12, 2018.

L. Chen, J. Zeng, and N. Tokuda, A ?stereo? document representation for textual information retrieval, Journal of the American Society for Information Science and Technology, vol.57, issue.6, pp.768-774, 2006.

Y. Chen, X. Wang, and B. Liu, Multi-document summarization based on Lexical chains, Proceedings of 2005 International Conference on Machine Learning and Cybernetics, pp.1937-1942, 2005.

Y. Chen and M. Bansal, Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol.1, pp.675-686, 2018.

Y. Chen, J. Pu, X. Liu, and X. Zhang, Gaussian mixture embedding of multiple node roles in networks, World Wide Web, vol.23, issue.2, pp.927-950, 2019.

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 (EMNLP), 2014.
URL : https://hal.archives-ouvertes.fr/hal-01433235

N. Chomsky, Rules and representations, Behavioral and Brain Sciences, vol.3, issue.1, pp.1-15, 1980.

G. Chrupa?a, À. Kádár, and A. Alishahi, Learning language through pictures, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), vol.2, pp.112-118, 2015.

K. Clark, M. Luong, Q. V. Le, and C. D. Manning, ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators, International Conference on Learning Representations, 2019.

C. L. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan et al., Novelty and diversity in information retrieval evaluation, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08, pp.659-666, 2008.

C. Cleverdon, The CRANFIELD TESTS ON INDEX LANGUAGE DEVICES, Aslib Proceedings, vol.19, issue.6, pp.173-194, 1967.

G. Collell and M. Moens, Do Neural Network Cross-Modal Mappings Really Bridge Modalities?, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol.2, pp.462-468, 2018.

G. Collell, L. Van-gool, and M. Moens, Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

G. Collell, T. Zhang, and M. Moens, Imagined Visual Representations as Multimodal Embeddings, pp.4378-4384, 2017.

G. Collell and M. Moens, Learning Representations Specialized in Spatial Knowledge: Leveraging Language and Vision, Transactions of the Association for Computational Linguistics, vol.6, pp.133-144, 2018.

R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu et al., Natural language processing (almost) from scratch, pp.2493-2537, 2011.

A. Conneau, D. Kiela, H. Schwenk, L. Barrault, and A. Bordes, Supervised Learning of Universal Sentence Representations from Natural Language Inference Data, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01897968

A. Conneau, G. Lample, M. Ranzato, L. Denoyer, and H. Jégou, Word Translation Without Parallel Data, 2017.

C. Pereira, J. , E. Coviello, G. Doyle, N. Rasiwasia et al., On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.3, pp.521-535, 2014.

S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman, Indexing by latent semantic analysis, 1990.

J. Delbrouck, S. Dupont, and O. Seddati, Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation, GLU 2017 International Workshop on Grounding Language Understanding, 2017.

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A Large-Scale Hierarchical Image Database, 2009.

R. Deveaud, Vers une représentation du contexte thématique en Recherche d'Information, 2013.

R. Deveaud, E. Sanjuan, and P. Bellot, Are Semantically Coherent Topic Models Useful for Ad Hoc Information Retrieval?, In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, vol.2, pp.148-152, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01314959

J. Devlin, M. Chang, K. Lee, and K. Toutanova, 10.3726/978-3-653-04805-6/10, Inactive DOIs, vol.10

R. Devooght, A. Mantrach, I. Kivimäki, H. Bersini, A. Jaimes et al., Random walks based modularity, Proceedings of the 23rd international conference on World wide web - WWW '14, pp.213-224, 2014.

S. K. Divvala, A. Farhadi, and C. Guestrin, Learning Everything about Anything: Webly-Supervised Visual Concept Learning, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.3270-3277, 2014.

D. Santos, L. , B. Piwowarski, and P. Gallinari, Multilabel Classification on Heterogeneous Graphs with Gaussian Embeddings, pp.606-622, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01352911

L. Dos-santos, B. Piwowarski, and P. Gallinari, Gaussian Embeddings for Collaborative Filtering, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1065-1068, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01582488

S. Dumais, The Effect of Accessing Nonmatching Documents on Relevance Feedback, In: ACM Transactions on Information Systems, vol.15, pp.137-153, 1997.

G. E. Dupret and B. Piwowarski, A user browsing model to predict search engine click data from past observations., Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08, 2008.

G. Erkan and D. R. Radev, LexRank: Graph-based Lexical Centrality as Salience in Text Summarization, Journal of Artificial Intelligence Research, vol.22, issue.1, pp.457-479, 2004.

H. Fang, S. Gupta, F. Iandola, R. K. Srivastava, L. Deng et al., From captions to visual concepts and back, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

W. T. Freeman and M. Roth, Orientation Histograms for Hand Gesture Recognition, International Workshop on Automatic Face and Gesture Recognition, vol.12, pp.296-301, 1995.

A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean et al., DeViSE: A Deep Visual-Semantic Embedding Model, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held, pp.2121-2129, 2013.

I. Frommholz, B. Larsen, B. Piwowarski, M. Lalmas, P. Ingwersen et al., Supporting polyrepresentation in a quantum-inspired geometrical retrieval framework, Proceeding of the third symposium on Information interaction in context - IIiX '10, 2010.

I. Frommholz, B. Piwowarski, M. Lalmas, and K. Van-rijsbergen, Processing Queries in Session in a Quantum-Inspired IR Framework, Lecture Notes in Computer Science, pp.751-754, 2011.

N. Fuhr and C. Buckley, A probabilistic learning approach for document indexing, ACM Transactions on Information Systems, vol.9, issue.3, pp.223-248, 1991.

K. Fukushima and S. Miyake, Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Recognition, vol.15, issue.6, pp.455-469, 1982.

B. Gallagher, H. Tong, T. Eliassi-rad, and C. Faloutsos, Using ghost edges for classification in sparsely labeled networks, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08, pp.256-264, 2008.

P. Genest, Génération de résumés par abstraction, 2013.

L. Getoor, Introduction to Statistical Relational Learning, 2007.

A. M. Glenberg and M. P. Kaschak, Grounding language in action, Psychonomic Bulletin & Review, vol.9, issue.3, pp.558-565, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00194100

X. Glorot, A. Bordes, and Y. Bengio, Deep Sparse Rectifier Neural Networks, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse, Generic text summarization using relevance measure and Latent Semantic Analysis, Proceedings of the 24 th annual international ACM SIGIR conference, pp.19-25, 2001.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, 2016.

J. Gordon, B. Durme-;-gori, M. , F. Scarselli, A. Tsoi et al., Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering, 2009.

E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths, Recasting Gradient-Based Meta-Learning as Hierarchical Bayes, 2018.

A. Graves, G. Wayne, and I. Danihelka, Logic and Conversation, Speech Acts, pp.41-58, 1975.

A. Grover and J. Leskovec, node2vec, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.855-864, 2016.

S. P. Gudder, Quantum Probability, 1988.

J. Guo, Y. Fan, Q. Ai, and W. B. Croft, A Deep Relevance Matching Model for Ad-hoc Retrieval, Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016.

S. Harabagiu and F. Lacatusu, Topic themes for multi-document summarization, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05, pp.202-209, 2005.

J. He, V. Hollink, and A. De-vries, Combining implicit and explicit topic representations for result diversification, Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12, 2012.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

S. He, K. Liu, G. Ji, and J. Zhao, Learning to Represent Knowledge Graphs with Gaussian Embedding, Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15, 2015.

J. Hewitt and C. D. Manning, Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers on XX - NAACL '07, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1, pp.4129-4138, 2007.

F. Hill, K. Cho, and A. Korhonen, Learning Distributed Representations of Sentences from Unlabelled Data, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.1367-1377, 2016.

F. Hill and A. Korhonen, Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.255-265, 2014.

F. Hill and A. Korhonen, Learning Abstract Concept Embeddings from Multi-Modal Data: Since You Probably Can't See What I Mean, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.255-265, 2014.

F. Hill, R. Reichart, and A. Korhonen, Multi-Modal Models for Concrete and Abstract Concept Meaning, Transactions of the Association for Computational Linguistics, vol.2, pp.285-296, 2014.

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

E. Hoenkamp, Trading Spaces: On the Lore and Limitations of Latent Semantic Analysis, Lecture Notes in Computer Science, pp.40-51, 2011.

R. Hoffmann, C. Zhang, X. Ling, L. Zettlemoyer, and D. S. Weld, Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp.541-550, 2011.

T. Hofmann, R. Hu, J. Andreas, M. Rohrbach, T. Darrell et al., Learning to Reason: End-to-End Module Networks for Visual Question Answering, 2017 IEEE International Conference on Computer Vision (ICCV), pp.804-813, 2001.

P. Huang, X. He, J. Gao, L. Deng, A. Acero et al., Learning deep structured semantic models for web search using clickthrough data, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13, 2013.

Y. Y. Huang and W. Y. Wang, Deep Residual Learning for Weakly-Supervised Relation Extraction, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017.

Á. F. Huertas-rosero, L. Azzopardi, and C. J. Van-rijsbergen, Selective Erasers: A Theoretical Framework for Representing Documents Inspired by Quantum Theory, 2008.

T. Hwang and R. Kuang, A Heterogeneous Label Propagation Algorithm for Disease Gene Discovery, Proceedings of the 2010 SIAM International Conference on Data Mining, p.12, 2010.

P. Ingwersen and K. Järvelin, The Turn, 2005.

M. Iyyer, J. L. Boyd-graber, L. M. Claudino, R. Socher, and H. Daumé-iii, A Neural Network for Factoid Question Answering over Paragraphs, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.633-644, 2014.

M. Iyyer, V. Manjunatha, J. Boyd-graber, and H. Daumé-iii, Deep Unordered Composition Rivals Syntactic Methods for Text Classification, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2015.

, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol.1, pp.1681-1691, 2015.

Y. Jacob, L. Denoyer, and P. Gallinari, Learning latent representations of nodes for classifying in heterogeneous social networks, Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14, pp.1-10, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01212733

M. Ji, Y. Sun, M. Danilevsky, J. Han, and J. Gao, Graph Regularized Transductive Classification on Heterogeneous Information Networks, Machine Learning and Knowledge Discovery in Databases, pp.570-586, 2010.

M. Ji, Y. Sun, M. Danilevsky, J. Han, and J. Gao, Graph Regularized Transductive Classification on Heterogeneous Information Networks, Machine Learning and Knowledge Discovery in Databases, vol.0053, pp.570-586, 2010.

Y. Jiang, J. Yang, C. Ngo, and A. G. Hauptmann, Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study, IEEE Trans. Multimedia, vol.12, pp.42-53, 2010.

D. D. Johnson, Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets, 2015.

N. Kalchbrenner, E. Grefenstette, and P. Blunsom, A Convolutional Neural Network for Modelling Sentences, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014.

H. Kamp and U. Reyle, From Discourse to Logic: Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory, vol.717, 1993.

A. Karpathy, A. Joulin, and F. F. Li, Deep fragment embeddings for bidirectional image sentence mapping, Advances in neural information processing systems, pp.1889-1897, 2014.

A. Karpathy and L. Fei-fei, Deep visual-semantic alignments for generating image descriptions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3128-3137, 2015.

A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret, Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention, 2020.

G. Kazai, INitiative for the Evaluation of XML Retrieval, Encyclopedia of Database Systems, pp.1531-1537, 2009.

T. Kenter, A. Borisov, and M. De-rijke, Siamese CBOW: Optimizing Word Embeddings for Sentence Representations, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016.

D. Kiela and L. Bottou, Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.

D. Kiela, A. Conneau, A. Jabri, and M. Nickel, Learning Visually Grounded Sentence Representations, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), vol.1, pp.408-418, 2018.

Y. Kim and S. Choi, Bayesian binomial mixture model for collaborative prediction with non-random missing data, Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14, pp.201-208, 2014.

R. Kiros, Y. Zhu, R. Salakhutdinov, R. S. Zemel, R. Urtasun et al., Skip-Thought Vectors, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, pp.3294-3302, 2015.

N. Kitaev, ?. Kaiser, and A. Levskaya, arXiv moves, Materials Today, vol.4, issue.5, p.20, 2001.

B. Klein, G. Lev, G. Sadeh, and L. Wolf, Associating neural word embeddings with deep image representations using Fisher Vectors, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4437-4446, 2015.

S. Kok and P. Domingos, Extracting Semantic Networks from Text Via Relational Clustering, Machine Learning and Knowledge Discovery in Databases, pp.624-639

S. Kottur, R. Vedantam, J. M. Moura, and D. Parikh, VisualWord2Vec (Vis-W2V): Learning Visually Grounded Word Embeddings Using Abstract Scenes, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4985-4994, 2016.

O. Kovaleva, A. Romanov, A. Rogers, and A. Rumshisky, Revealing the Dark Secrets of BERT, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp.4364-4373, 2019.

Z. Kraljevic, N. Baskiotis, B. Piwowarski, and P. Gallinari, Représentation Temporelle Des Mots : Application Au Clustering de Micro-Blogs, Conférence En Recherche d'Infomations et Applications, pp.531-544, 2016.

R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata et al., Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations, International Journal of Computer Vision, vol.123, issue.1, pp.32-73, 2017.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Communications of the ACM, vol.60, issue.6, pp.84-90, 2017.

A. Kumar, O. ?rsoy, J. Su, J. Bradbury, R. English et al., Ask Me Anything: Dynamic Memory Networks for Natural Language Processing, 2015.

S. Kumar and Y. Tsvetkov, Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs, International Conference on Learning Representations, 2018.

Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma et al., ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, International Conference on Learning Representations, 2020.

G. R. Lanckriet, N. Cristianini, P. L. Bartlett, L. E. Ghaoui, and M. I. Jordan, Learning the Kernel Matrix with Semidefinite Programming, The Journal of Machine Learning Research, pp.27-72, 2004.

A. Lazaridou, N. T. Pham, and M. Baroni, Combining Language and Vision with a Multimodal Skip-gram Model, Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.153-163, 2015.

Q. V. Le and T. Mikolov, Distributed Representations of Sentences and Documents, Proceedings of the 31st International Conference on Machine Learning, 2014.

L. Digabel and S. , Algorithm 909, ACM Transactions on Mathematical Software, vol.37, issue.4, pp.1-15, 2011.

Y. Lecun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard et al., Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, pp.541-551, 1989.

O. Levy and Y. Goldberg, A Matrix Factorization Technique with Word Embedding for Recommendation, Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering, 2020.

M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed et al., BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020.

J. Li and L. Sun, A Lexical Chain Approach for Update-Style Query-Focused Multi-document Summarization, Information Retrieval Technology, pp.310-320

Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, Gated Graph Sequence Neural Networks, 2015.

Y. Li, Y. Song, L. Cao, J. Tetreault, L. Goldberg et al., TGIF: A New Dataset and Benchmark on Animated GIF Description, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

T. Lin, M. Maire, S. J. Belongie, J. Hays, P. Perona et al., Microsoft COCO: Common Objects in Context, Computer Vision ? ECCV 2014, pp.740-755, 2014.

Y. Lin, S. Shen, Z. Liu, H. Luan, and M. Sun, Neural Relation Extraction with Selective Attention over Instances, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol.1, pp.2124-2133, 2016.

T. Liu, Applications of Learning to Rank, Learning to Rank for Information Retrieval, pp.181-191, 2011.

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi et al., RoBERTa: A Robustly Optimized BERT Pretraining Approach, 2019.

L. Logeswaran and H. Lee, An efficient framework for learning sentence representations, 2018.

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, vol.2, pp.1150-1157, 1999.

Q. Lu and L. Getoor, Link-based classification, In: ICML, vol.3, pp.496-503, 2003.

Y. M. Lu and M. N. Do, Multidimensional Directional Filter Banks and Surfacelets, IEEE Transactions on Image Processing, vol.16, issue.4, pp.918-931, 2007.

J. Luketina, T. Raiko, M. Berglund, and K. Greff, Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters, Proceedings of the 33nd International Conference on Machine Learning, pp.2952-2960, 2016.

L. V. Maaten and G. Hinton, Visualizing data using t-SNE, In: Journal of machine learning research, vol.9, pp.2579-2605, 2008.

E. Mansimov, E. Parisotto, J. Ba, and R. Salakhutdinov, Generating Images from Captions with Attention, 2015.

D. Marcheggiani and I. Titov, Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations, Transactions of the Association for Computational Linguistics, vol.4, pp.231-244, 2016.

L. Martin, B. Muller, P. J. Ortiz-suárez, Y. Dupont, L. Romary et al., CamemBERT: a Tasty French Language Model, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02445946

T. Mccoy, E. Pavlick, and T. Linzen, Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.3428-3448, 2019.

K. Mcrae, G. S. Cree, M. S. Seidenberg, and C. Mcnorgan, Semantic feature production norms for a large set of living and nonliving things, Behavior Research Methods, vol.37, issue.4, pp.547-559, 2005.

M. Melucci, Combining the language model and inference network approaches to retrieval, In: Information Processing & Management. Bayesian Networks and Information Retrieval, vol.40, pp.735-750, 2004.

R. Mihalcea, Language independent extractive summarization, Proceedings of the ACL 2005 on Interactive poster and demonstration sessions - ACL '05, pp.49-52, 2005.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Distributed Inference in Dynamical Systems, Advances in Neural Information Processing Systems 19, pp.3111-3119, 2007.

B. Mitra and N. Craswell, An Introduction to Neural Information Retrieval t, 2018.

B. Mitra, F. Diaz, and N. Craswell, Learning to Match using Local and Distributed Representations of Text for Web Search, Proceedings of the 26th International Conference on World Wide Web, pp.1291-1299, 2017.

J. Moore and J. Neville, Deep collective inference, Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017.

M. Moradshahi, H. Palangi, M. S. Lam, P. Smolensky, and J. Gao, HUBERT Untangles BERT to Improve Transfer across NLP Tasks, 2019.

L. Moreno, G. Bavota, M. D. Penta, R. Oliveto, A. Marcus et al., ARENA: An Approach for the Automated Generation of Release Notes, IEEE Transactions on Software Engineering, vol.43, issue.2, pp.106-127, 2017.

Y. Moshfeghi, D. Agarwal, B. Piwowarski, and J. M. Jose, Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering, Lecture Notes in Computer Science, pp.54-65, 2009.

Y. Moshfeghi, B. Piwowarski, and J. M. Jose, Handling data sparsity in collaborative filtering using emotion and semantic based features, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11, p.84, 2011.

T. Mukherjee and T. Hospedales, Gaussian Visual-Linguistic Embedding for Zero-Shot Recognition, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.

C. Müller and I. Gurevych, Using Wikipedia and Wiktionary in Domain-Specific Information Retrieval, Lecture Notes in Computer Science, pp.219-226, 2009.

K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012.

G. Murray, S. Renals, J. Carletta, and J. Moore, Incorporating speaker and discourse features into speech summarization, Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -, pp.593-596, 2006.

E. Nalisnick, B. Mitra, N. Craswell, and R. Caruana, Improving Document Ranking with Dual Word Embeddings, Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion, pp.83-84, 2016.

S. Nandanwar and M. N. Murty, Structural Neighborhood Based Classification of Nodes in a Network, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1085-1094, 2016.

D. L. Nelson, C. L. Mcevoy, and T. A. Schreiber, The University of South Florida free association, rhyme, and word fragment norms, Behavior Research Methods, Instruments, & Computers, vol.36, issue.3, pp.402-407, 2004.

J. Neville and D. Jensen, Iterative classification in relational data, Proc. AAAI-2000 Workshop on Learning Statistical Models from Relational Data, pp.13-20, 2000.

A. Y. Ng, R. Socher, C. D. Manning, J. Pennington, and E. H. Huang, Semisupervised recursive autoencoders for predicting sentiment distributions, Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 2011.

M. Niepert, M. Ahmed, and K. Kutzkov, Deep Learning Convolutional Neural Network, Deep Learning Neural Networks, pp.41-55, 2016.

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, pp.4658-4664, 2019.

M. Norouzi, T. Mikolov, S. Bengio, Y. Singer, J. Shlens et al., BACTERIOPHAGE IN CHOLERA., The Lancet, vol.218, issue.5650, pp.1311-1312, 1931.

M. G. Ozsoy, F. N. Alpaslan, and I. Cicekli, Text summarization using Latent Semantic Analysis, Journal of Information Science, vol.37, issue.4, pp.405-417, 2011.

L. Page, S. Brin, R. Motwani, and T. Winograd, PageRank Algorithm, 1998; Brin, Page

R. Pasunuru and M. Bansal, Multi-Reward Reinforced Summarization with Saliency and Entailment, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), 2018.

Y. Pei, X. Du, J. Zhang, G. Fletcher, and M. Pechenizkiy, struc2gauss: Structural role preserving network embedding via Gaussian embedding, Data Mining and Knowledge Discovery, vol.34, issue.4, pp.1072-1103, 2020.

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 (EMNLP), 2014.

B. Perozzi, R. Al-rfou, and S. Skiena, DeepWalk, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14, pp.701-710, 2014.

M. E. 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, Volume 1 (Long Papers), 2018.

S. Peters, L. Denoyer, and P. Gallinari, Iterative Annotation of Multi-relational Social Networks, 2010 International Conference on Advances in Social Networks Analysis and Mining, pp.96-103, 2010.
URL : https://hal.archives-ouvertes.fr/hal-01357569

T. Pham, T. Tran, D. Phung, and S. Venkatesh, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

R. Pimplikar, D. Garg, D. Bharani, and G. Parija, Learning to Propagate Rare Labels, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14, pp.201-210, 2014.

B. Piwowarski, Experimaestro and Datamaestro, Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02989011

B. Piwowarski, M. R. Amini, and M. Lalmas, On using a quantum physics formalism for multidocument summarization, Journal of the American Society for Information Science and Technology, vol.63, issue.5, pp.865-888, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01172685

B. Piwowarski and G. Dupret, Evaluation in (XML) information retrieval, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06, pp.260-267, 2006.

B. Piwowarski, G. Dupret, and R. Jones, Mining user web search activity with layered bayesian networks or how to capture a click in its context, Proceedings of the Second ACM International Conference on Web Search and Data Mining - WSDM '09, pp.162-171, 2009.

B. Piwowarski, I. Frommholz, M. Lalmas, and K. Van-rijsbergen, What can quantum theory bring to information retrieval, Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10, 2010.

B. Piwowarski, I. Frommholz, Y. Moshfeghi, M. Lalmas, and K. Van-rijsbergen, Filtering Documents with Subspaces, Lecture Notes in Computer Science, vol.5993, pp.615-618, 2010.

B. Piwowarski, P. Gallinari, and G. Dupret, Precision recall with user modeling (PRUM), ACM Transactions on Information Systems, vol.25, issue.1, p.1, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01172395

B. Piwowarski and M. Lalmas, Structured Information Retrieval and Quantum Theory, Quantum Interaction, vol.5766, pp.289-298, 2009.

B. Piwowarski and H. Zaragoza, Predictive user click models based on click-through history, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management - CIKM '07, pp.175-182, 2007.

B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier et al., Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models, 2015 IEEE International Conference on Computer Vision (ICCV), pp.2641-2649, 2015.

L. Polanyi and A. Zaenen, Contextual Valence Shifters, The Information Retrieval Series, vol.20, pp.1-10

A. Purpura, M. Maggipinto, G. Silvello, and G. A. Susto, Probabilistic Word Embeddings in Neural IR, Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, pp.3-10, 2019.

G. Qiu, Indexing chromatic and achromatic patterns for content-based colour image retrieval, Pattern Recognition, vol.35, issue.8, pp.1675-1686, 2002.

X. Qiu and X. Huang, Convolutional Neural Tensor Network Architecture for Community-Based Question Answering, IJCAI, pp.1305-1311, 2015.

D. R. Radev, H. Jing, M. Sty?, and D. Tam, Centroid-based summarization of multiple documents, Information Processing & Management, vol.40, issue.6, pp.919-938, 2004.

A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, Improving Language Understanding by Generative Pre-Training, 2018.

J. Radford and M. Mahon, Editorial, Child Language Teaching and Therapy, vol.24, issue.2, pp.127-129, 2008.

J. W. Rae and A. Razavi, Do Transformers Need Deep Long-Range Memory?, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020.

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang et al., Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, 2019.

B. Rekabdar, C. Mousas, and B. Gupta, Generative Adversarial Network with Policy Gradient for Text Summarization, 2019 IEEE 13th International Conference on Semantic Computing (ICSC), pp.204-207, 2019.

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-thieme, BPR: Bayesian personalized ranking from implicit feedback, In: Uncertainty in Artificial Intelligence, pp.452-461, 2009.

F. Ricci, L. Rokach, B. Shapira, K. Paul, and B. , Recommender Systems Handbook, vol.532, 2011.

K. Rijsbergen and . Van, The Geometry of Information Retrieval, 2004.

S. E. Robertson and S. Walker, Some Simple Effective Approximations to the 2-Poisson Model for Probabilistic Weighted Retrieval, SIGIR ?94, pp.232-241, 1994.

S. E. Robertson and H. Zaragoza, The Probabilistic Relevance Framework: BM25 and Beyond, Foundations and Trends® in Information Retrieval, vol.3, issue.4, pp.333-389, 2009.

A. Rogers, O. Kovaleva, A. Rumshisky-;-rohrbach, A. , M. Rohrbach et al., A Primer in BERTology: What we know about how BERT works, Computer Vision -ECCV 2016 -14th European Conference, pp.817-834, 2016.

S. Roller and S. Schulte-im-walde, A Multimodal LDA Model Integrating Textual, Cognitive and Visual Modalities, Emnlp October, pp.1146-1157, 2013.

A. Rosenberg and J. Hirschberg, V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure, 2007.

M. Rudolph and D. Blei, Dynamic Embeddings for Language Evolution, Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18, 2018.

M. R. Rudolph and D. M. Blei, Dynamic Embeddings for Language Evolution, Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18, 2018.

A. Saha and V. Sindhwani, Learning evolving and emerging topics in social media, Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12, pp.693-702, 2012.

S. K. Sahu, A. Anand, K. Oruganty, and M. Gattu, Relation extraction from clinical texts using domain invariant convolutional neural network, Proceedings of the 15th Workshop on Biomedical Natural Language Processing, vol.20, pp.1257-1264, 2016.

G. Salton and M. E. Lesk, The SMART automatic document retrieval systems?an illustration, Communications of the ACM, vol.8, issue.6, pp.391-398, 1965.

G. Salton, A. Wong, and C. S. Yang, A vector space model for automatic indexing, Communications of the ACM, vol.18, issue.11, pp.613-620, 1975.

V. Sanh, L. Debut, J. Chaumond, and T. Wolf, Lighter, faster, cheaper detection of radiocarbon, Physics Today, 2016.

C. N. Santos, B. Xiang, and B. Zhou, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

L. D. Santos, B. Piwowarski, L. Denoyer, and P. Gallinari, Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks, ACM Transactions on Knowledge Discovery from Data, vol.12, issue.5, pp.1-33, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01853928

R. L. Santos, J. Peng, C. Macdonald, and I. Ounis, Explicit Search Result Diversification through Sub-queries, Lecture Notes in Computer Science, pp.87-99, 2010.

J. Schulman, N. Heess, T. Weber, and P. Abbeel, Gradient Estimation Using Stochastic Computation Graphs, 2015.

M. Schuster and K. K. Paliwal, Bidirectional recurrent neural networks, IEEE Transactions on Signal Processing, vol.45, issue.11, pp.2673-2681, 1997.

H. Schütze, The Morgan Kaufmann Series in Data Management Systems, Transactional Information Systems, pp.ii-iii, 2002.

H. Schwenk, Continuous space language models, Computer Speech & Language, vol.21, issue.3, pp.492-518, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01434560

T. Scialom, S. Lamprier, B. Piwowarski, and J. Staiano, Answers Unite! Unsupervised Metrics for Reinforced Summarization Models, 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.
URL : https://hal.archives-ouvertes.fr/hal-02350999

T. Scialom, S. Lamprier, B. Piwowarski, and J. Staiano, Answers Unite! Unsupervised Metrics for Reinforced Summarization Models, 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.
URL : https://hal.archives-ouvertes.fr/hal-02350999

A. See, P. J. Liu, and C. D. Manning, Get To The Point: Summarization with Pointer-Generator Networks, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017.

P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher et al., Collective Classification in Network Data, AI Magazine, vol.29, issue.3, p.93, 2008.

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 (Volume 1: Long Papers), 2016.

R. Shekhar, S. Pezzelle, Y. Klimovich, A. Herbelot, M. Nabi et al., FOIL it! Find One mismatch between Image and Language caption, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol.1, pp.255-265, 2017.

C. Silberer and M. Lapata, Learning Grounded Meaning Representations with Autoencoders, Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014.

É. Simon, V. Guigue, and B. Piwowarski, Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02318233

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014.

R. Socher, A. Perelygin, J. Wu, and J. Chuang, Recursive deep models for semantic compositionality over a sentiment treebank, 2013.

A. Sordoni, J. Nie, and Y. Bengio, Modeling term dependencies with quantum language models for IR, Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '13, 2013.

A. Sordoni, J. He, and J. Nie, Modeling latent topic interactions using quantum interference for information retrieval, Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13, 2013.

N. Srivastava, I. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A Simple Way to Prevent Neural Networks from Overfitting, 2014.

N. Srivastava, E. Mansimov, and R. Salakhutdinov, Unsupervised Learning of Video Representations using LSTMs, 2015.

R. Srivastava, K. Greff, and J. Schmidhuber, Max-Margin Incremental CCG Parsing, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp.4111-4122, 2015.

M. Steedman and J. Baldridge, Combinatory Categorial Grammar, Encyclopedia of Language & Linguistics, pp.610-621, 2006.

D. H. Stern, R. Herbrich, and T. Graepel, Matchbox, Proceedings of the 18th international conference on World wide web - WWW '09, 2009.

W. Su, X. Zhu, Y. Cao, B. Li, L. Lu et al., VL-BERT: Pre-Training of Generic Visual-Linguistic Representations, International Conference on Learning Representations, 2019.

S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus, End-To-End Memory Networks, 2015.

M. Surdeanu, J. Tibshirani, R. Nallapati, and C. D. Manning, Multi-instance multilabel learning for relation extraction, Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pp.455-465, 2012.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the Inception Architecture for Computer Vision, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan et al., LINE, Proceedings of the 24th International Conference on World Wide Web - WWW '15, pp.1067-1077, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00777489

M. Taylor, J. Guiver, S. Robertson, and T. Minka, SoftRank, Proceedings of the international conference on Web search and web data mining - WSDM '08, 2008.

S. Teufel and H. Van-halteren, Evaluating information content by factoid analysis: human annotation and stability, Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, p.8, 2004.

H. Titeux, B. Piwowarski, and P. Gallinari, Max-Margin DeepWalk: Discriminative Learning of Network Representation, Conférence en Recherche d'Information et Applications, pp.3889-3895, 2016.

F. Vasile, E. Smirnova, and A. Conneau, Meta-Prod2Vec, Proceedings of the 10th ACM Conference on Recommender Systems, pp.225-232, 2016.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Attention Is All You Need, 2017.

R. Vedantam, X. Lin, T. Batra, C. L. Zitnick, and D. Parikh, Learning Common Sense through Visual Abstraction, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.

R. Verma and D. Lee, Extractive Summarization: Limits, Compression, Generalized Model and Heuristics, Computación y Sistemas, vol.21, issue.4, 2018.

L. Vilnis, X. Li, S. Murty, and A. Mccallum, Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018.

O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, Show and tell: A neural image caption generator, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

D. Wang, T. Li, S. Zhu, and C. Ding, Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08, pp.307-314, 2008.

D. Wang, P. Cui, and W. Zhu, Structural Deep Network Embedding, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1225-1234, 2016.

J. Wang, T. Jebara, and S. Chang, Graph transduction via alternating minimization, Proceedings of the 25th international conference on Machine learning - ICML '08, pp.1144-1151, 2008.

J. Wang and J. Zhu, Portfolio theory of information retrieval, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09, 2009.

L. Wang, Y. Li, and S. Lazebnik, Learning Deep Structure-Preserving Image-Text Embeddings, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5005-5013, 2016.

S. Wang, B. Z. Li, M. Khabsa, H. Fang, and H. Ma, Linformer: Self-Attention with Linear Complexity, 2020.

W. Wang, N. Yang, F. Wei, B. Chang, and M. Zhou, Gated Self-Matching Networks for Reading Comprehension and Question Answering, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol.1, pp.189-198, 2017.

X. Wang and G. Sukthankar, Multi-label relational neighbor classification using social context features, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13, pp.464-472, 2013.

X. Wang and A. Mccallum, Topics over time, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06, pp.424-433, 2006.

Y. Wang, E. Agichtein, and M. Benzi, TM-LDA, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12, pp.123-131, 2012.

M. Weimer, A. Karatzoglou, and A. Smola, Improving maximum margin matrix factorization, Machine Learning, vol.72, issue.3, pp.263-276, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00482747

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

J. Weston, B. Schölkopf, and G. H. Bakir, Learning to Find Pre-Images, Advances in Neural Information Processing Systems, vol.16, pp.449-456, 2004.

K. Woodsend and M. Lapata, Multiple Aspect Summarization Using Integer Linear Programming, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational NaturalLanguage Learning, p.11, 2012.

L. Xia, J. Xu, Y. Lan, J. Guo, W. Zeng et al., Adapting Markov Decision Process for Search Result Diversification, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.535-544, 2017.

R. Xiang and J. Neville, Collective inference for network data with copula latent markov networks, Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13, pp.647-656, 2013.

K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville et al., Show, Attend and Tell -Neural Image Caption Generation with Visual Attention, 2015.

Y. Xu, L. Mou, G. Li, Y. Chen, H. Peng et al., Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp.1785-1794, 2015.

B. Yang, X. W.-t.-yih, J. He, L. Gao, and . Deng, Learning Multi-Relational Semantics Using Neural-Embedding Models, 2014.

J. Yang and J. Leskovec, Patterns of temporal variation in online media, Proceedings of the fourth ACM international conference on Web search and data mining - WSDM '11, 2011.

W. Yang, H. Zhang, and J. Lin, Simple Applications of BERT for Ad Hoc Document Retrieval, 2019.

Z. Yang, W. Cohen, and R. Salakhutdinov, Revisiting Semi-Supervised Learning with Graph Embeddings, 2016.

L. Yao, A. Haghighi, S. Riedel, and A. Mccallum, Structured Relation Discovery using Generative Models, 2011.

L. Yao, S. Riedel, and A. Mccallum, Unsupervised Relation Discovery with Sense Disambiguation, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, vol.1, pp.712-720, 2012.

M. Yatskar, V. Ordonez, and A. Farhadi, Stating the Obvious: Extracting Visual Common Sense Knowledge, Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.

J. Ye and L. Akoglu, Preprint repository arXiv achieves milestone million uploads, Physics Today, 2014.

W. Yin, H. Schütze, B. Xiang, and B. Zhou, ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs, Transactions of the Association for Computational Linguistics, vol.4, pp.259-272, 2016.

L. Yu, W. Zhang, J. Wang, and Y. Yu, 10.3726/978-3-653-05473-6/18, Inactive DOIs
URL : https://hal.archives-ouvertes.fr/hal-00631957

E. Zablocki, P. Bordes, L. Soulier, B. Piwowarski, and P. Gallinari, Context-Aware Zero-Shot Learning for Object Recognition, International Conference on Machine Learning, pp.7292-7303, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02116654

P. Bordes, E. Zablocki, L. Soulier, B. Piwowarski, and P. Gallinari, Incorporating Visual Semantics into Sentence Representations within a Grounded Space, 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.
URL : https://hal.archives-ouvertes.fr/hal-02351003

É. Zablocki, B. Piwowarski, L. Soulier, and P. Gallinari, Learning Multi-Modal Word Representation Grounded in Visual Context." en, Proceedings of the Association for the Advancement of Artificial Intelligence, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01632414

M. D. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, Computer Vision -ECCV 2014 -13th European Conference, pp.818-833, 2014.

D. Zeng, K. Liu, Y. Chen, and J. Zhao, Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015.

F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W. Ma, Collaborative Knowledge Base Embedding for Recommender Systems, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.

W. Zhang, Y. Yu, B. Zhou, Y. Zhang, K. Liu et al., Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Information, 2015.

Z. Zhang, Weakly-supervised relation classification for information extraction, Proceedings of the Thirteenth ACM conference on Information and knowledge management - CIKM '04, pp.581-588, 2004.

J. Zhao and Y. Yun, A proximity language model for information retrieval, Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09, p.291, 2009.

D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf, Advances in Neural Information Processing Systems 19, Proceedings of the 16th International Conference on Neural Information Processing Systems. NIPS'03, pp.321-328, 2007.

D. Zhou, J. Huang, and B. Schölkopf, Learning from labeled and unlabeled data on a directed graph, Proceedings of the 22nd international conference on Machine learning - ICML '05, pp.1036-1043, 2005.

Q. Zhou, N. Yang, F. Wei, and M. Zhou, Selective Encoding for Abstractive Sentence Summarization, Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017.

Y. Zhou and L. Liu, Activity-edge centric multi-label classification for mining heterogeneous information networks, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14, pp.1276-1285, 2014.

X. Zhu and T. T. Rogers, A Cognitive Study of Learning with Labeled and Unlabeled Data, Tech. rep. Citeseer, 2012.

Y. Zhu, O. Groth, M. Bernstein, and L. Fei-fei, Visual7W: Grounded Question Answering in Images, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

Y. Zhu, J. J. Lim, and L. Fei-fei, Knowledge Acquisition for Visual Question Answering via Iterative Querying, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

A. Ziat, E. Delasalles, L. Denoyer, and P. Gallinari, Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery, 2017 IEEE International Conference on Data Mining (ICDM), 2017.
URL : https://hal.archives-ouvertes.fr/hal-02297513

G. Zuccon, L. A. Azzopardi, and K. Van-rijsbergen, The Quantum Probability Ranking Principle for Information Retrieval, Lecture Notes in Computer Science, pp.232-240, 2009.

G. Zuccon, L. A. Azzopardi, and C. J. Van-rijsbergen, Semantic Spaces: Measuring the Distance between Different Subspaces, Quantum Interaction, pp.225-236, 2009.

G. Zuccon, B. Piwowarski, and L. Azzopardi, On the use of Complex Numbers in Quantum Models for Information Retrieval, Lecture Notes in Computer Science, pp.346-350, 2011.