Deep learning pour la RI © Some slides are directly extracted from http://nn4ir.com/ From traditional to modern approaches in IR o Traditional approach RSV(d, q) o Modern approach (guided by machine learning) f(d, q) n n n n Conducting query and document understanding Representing query and document as multiple feature vectors Calculating multiple matching scores between query and document Training ranker with matching scores as features using learning to rank Recherche d'information textuelle 266 © http://nn4ir.com/ Lexical vs. Semantic matching Recherche d'information textuelle 267 Deep learning for semantic matching o Pioneer work for text semantics n Word embeddings o Skip-gram & CBOW o Following methods: Gloves, FastText, … o Principle: learn word representations using contextual informations o Unsupervised methods (not task-driven) Recherche d'information textuelle 268 © http://nn4ir.com/ From Word to Document/Query embeddings Recherche d'information textuelle 269 © http://nn4ir.com/ From Word to Document/Query embeddings Recherche d'information textuelle 270 © http://nn4ir.com/ Dual Embedding Space Model (DESM) [Nalisnick et al., 2016] Recherche d'information textuelle 271 © http://nn4ir.com/ Dual Embedding Space Model (DESM) [Nalisnick et al., 2016] Recherche d'information textuelle 272 © http://nn4ir.com/ Neural Translation Language Model [Zuccon et al., 2015] © Zuccon et al. 2015 Recherche d'information textuelle 273 © http://nn4ir.com/ Neural Translation Language Model [Zuccon et al., 2015] Recherche d'information textuelle 274 © http://nn4ir.com/ Pre-trained word embedding for query term weighting [Zheng and Callan, 2015] Recherche d'information textuelle 275 © http://nn4ir.com/ Pre-trained word embedding for query expansion Recherche d'information textuelle 276 © http://nn4ir.com/ Pre-trained word embedding for query expansion Recherche d'information textuelle 277 From document/query represe

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