E. Agirre, X. Arregi, and A. Otegi, « Document expansion based on WordNet for robust IR, pp.9-17, 2010.

Q. Ai, L. Yang, J. Guo, and W. B. Croft, Improving Language Estimation with the Paragraph Vector Model for Ad-hoc Retrieval, Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR '16, pp.869-872, 2016.
DOI : 10.1145/2911451.2914688

J. Bai, D. Song, P. Bruza, J. Nie, and G. Cao, Query expansion using term relationships in language models for information retrieval, Proceedings of the 14th ACM international conference on Information and knowledge management , CIKM '05, 2005.
DOI : 10.1145/1099554.1099725

G. W. Furnas, S. Deerwester, S. T. Dumais, T. K. Landauer, R. A. Harshman et al., Information retrieval using a singular value decomposition model of latent semantic structure, Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '88, pp.465-480, 1988.
DOI : 10.1145/62437.62487

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, p.2013
DOI : 10.1145/2505515.2505665

I. Iacobacci, M. T. Pilehvar, R. Navigli, and . Sensembed, SensEmbed: Learning Sense Embeddings for Word and Relational Similarity, 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), pp.95-105, 2015.
DOI : 10.3115/v1/P15-1010

C. Leacock and M. Chodorow, Combining local context and WordNet similarity for word sense identification », WordNet: An electronic lexical database, pp.265-283, 1998.

Z. Lu and H. Li, « A deep architecture for matching short texts, p.2013

T. Mikolov, K. Chen, G. Corrado, and J. Dean, « Efficient estimation of word representations in vector space, 2013.

B. Mitra, E. Nalisnick, N. Craswell, and R. Caruana, « A dual embedding space model for document ranking, 2016.

D. Pal, M. Mitra, and K. Datta, Improving query expansion using WordNet, Journal of the Association for Information Science and Technology, vol.18, issue.1, pp.2469-2478, 2014.
DOI : 10.1002/asi.23143
URL : http://arxiv.org/abs/1309.4938

T. Pedersen, V. Kolhatkar, and . Wordnet, WordNet::SenseRelate::AllWords, Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session on, NAACL '09, pp.17-20, 2009.
DOI : 10.3115/1620959.1620964

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), pp.1532-1543, 2014.
DOI : 10.3115/v1/D14-1162

A. Severyn and A. Moschitti, Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '15, pp.373-382, 2015.
DOI : 10.1145/2766462.2767738

Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil, A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM '14, p.2014
DOI : 10.1145/2661829.2661935

X. Wei and W. B. Croft, LDA-based document models for ad-hoc retrieval, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '06, pp.178-185, 2006.
DOI : 10.1145/1148170.1148204

C. Xiong, J. Callan, and . Esdrank, Connecting Query and Documents Through External Semi-Structured Data, pp.951-960, 2015.

X. Zhou, X. Zhang, X. Hu, and . Maxmatcher, MaxMatcher: Biological Concept Extraction Using Approximate Dictionary Lookup, 2006.
DOI : 10.1007/978-3-540-36668-3_150
URL : http://cci.drexel.edu/faculty/thu/research-papers/gu53mx33l8q04754.pdf