Thèse IRIT-Renault: biblio initiale
- [Deghani2017] Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, W. Bruce Croft. Neural Ranking Models with Weak Supervision. SIGIR 2017: 65-74 - [Faruqui2014] Faruqui M., Dodge J., Jauhar S. K., Dyer C., Hovy E., Smith N. A., « Retrofitting Word Vectors to Semantic Lexicons », NAACL, 2014 - [Moreno2017] Moreno, J. G., Besançon, R., Beaumont, R., D’hondt, E., Ligozat, A. L., Rosset, S., Grau, B. (2017, Combining word and entity embeddings for entity linking. In Extended Semantic Web Conference (ESWC) pp. 337-352, 2017 - [Nickel2017] Nickel, M., & Kiela, D. Poincaré embeddings for learning hierarchical representations. In Advances in Neural Information Processing Systems (pp. 6341-6350), 2017. - [Nguyen2017] Nguyen, G. H., Tamine, L., Soulier, L., & Souf, N. (2017, June). Learning Concept-Driven Document Embeddings for Medical Information Search. In Conference on Artificial Intelligence in Medicine in Europe (pp. 160-170). Springer, Cham - [Nguyen2018] Gia Nguyen, Lynda Tamine, Laure Soulier, Nathalie Souf, A Tri-Partite Neural Document Language Model for Semantic Information Retrieval. In Extended Semantic Web Conference (ESWC), 2018 [Yu2014] Yu M., Dredze M., « Improving Lexical Embeddings with Semantic Knowledge », ACL, p. 545- 550, 2014 - [Wang2014] Wang Z., Zhang J., Feng J., Chen Z., « Knowledge Graph and Text Jointly Embedding », EMNLP, p. 1591- 1601, 2014 - [Yamada2016] Yamada, I., Shindo, H., Takeda, H., Takefuji, Y., « Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation », CoNLL, p. 250-259, 2016
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