Vorschau wird generiert...
From traditional to modern approaches in IR o Traditional approach RSV(d,q) o Modern approach (guided by machine learning) f(d,q) n Conducting query and document understanding n Representing query and document as multiple feature vectors n Calculating multiple matching scores between query and document n Training ranker with matching scores as features using learning to rank Recherche d'information textuelle 266
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/ 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 representations to end-to-end neural IR models o Document/query representations are generally learnt independently from the retrieval task n Well designed for semantic matching n Not specifically designed for relevance matching o One solution is to guide representation according to the retrieval task n Goal of retrieval task: ranking n Need to build neural network able to rank documents (learning-to-rank) Recherche d'information textuelle 278
© Nuyen End-to-end neural IR approaches n Representation-based n Interaction-based neural neural models models © Nguyen 2018 Recherche d'information textuelle 279
Learning-to-rank with neural models o Triplet loss ©https://filebox.ece.vt.edu/~jbhuang/te aching/ece6554/sp17/lectures/Lecture_0 8_Siamese_Triplet_Networks.pdf ! = max(cos *,, + −cos *,, − +/01234,0) Recherche d'information textuelle 280
Representation-based neural models Deep Semantic Matching Model (Huang et al, 2014) Recherche d'information textuelle 281
Representation-based neural models Deep Semantic Matching Model (Huang et al, 2014) o Word representations: letter trigram o Document scoring o Loss Recherche d'information textuelle 282
Representation-based neural models Deep Semantic Matching Model (Huang et al, 2014) n Dataset: commercial search engine (over 16.000 queries) Recherche d'information textuelle 283
© http://nn4ir.com/ Representation-based neural models An extension with convolution (sentence level) Recherche d'information textuelle 284
Die ersten 20 Seiten werden angezeigt. Laden Sie das Dokument herunter, um alles zu sehen.
Noch keine Kommentare. Seien Sie der Erste!
Kommentare (0)
Noch keine Kommentare. Seien Sie der Erste!