This should be of interest to many of us, from our colleagues in the DIRO's NLP group.

---------- Forwarded message ---------- From: nie nie@iro.umontreal.ca Date: 2017-05-03 17:24 GMT-04:00 Subject: présentation prédoc III - Yifan Nie To: seminaires@iro.umontreal.ca

Vous êtes cordialement invités à cette présentation prédoc III, par:

Yifan Nie (RALI)

Tailored Deep Learning Techniques for Information Retrieval

Vendredi 5 mai 2017, 11:00, salle Z-200, pavillon Claire McNicoll

Traditional information retrieval systems employing bag-of-words representations often lead to poor results. More recently, learning-to-rank (L2R) approaches have successfully incorporated the idea of learning a ranking function. However, the features used to represent documents and queries in L2R are still human-crafted. In this research, we will incorporate representation learning developed in the area of deep learning. The proposed framework is tailored to IR tasks, which combines representation learning and ranking-function learning. To implement the framework, several key problems need to be addressed: 1. Query-dependent representation learning; 2. Using multiple representations in a deep net; 3. Generating pseudo training data for IR. In this presentation, we will propose approaches to these problems and report some preliminary results.

Jury:

Aaron Courville

Philippe Langlais Jian-Yun Nie (directeur)