Lecture 7: Probabilistic information retrieval, language models
Hedvig Kjellström

Lecture 9: Guest lectures

Anders Friberg, KTH                                                                                                  TITLE Abstract

Hercules Dalianis, SU                                                                                             Clinical text retrieval - some methods and some applications                       Electronic patient records contain a waste source of information, both in form of structured information as diagnosis codes, drug codes, lab values, time stamps, etc and unstructured in form of free text. Methods - both rule based and machine learning based for retrieving this information is presented. Applications as diagnosis codes assignment, hospital acquired infection detection and adverse drug event detection will be discussed.

Lecture 10: Guest lectures

Simon Stenström and Martin Nycander, Findwise                                               Search solutions from the Trenches                                                                            The presentation will describe the difference between a search index and a search solution and the process of building a search solution. We will show real world examples that explains some of the problems that we encounter when working with different parts of search solutions. We will discuss source data, what you should index, how you create a real search query and what you can create from a backing search engine.

Magnus Rosell, FOI                                                                                                   TITLE Abstract

Lecture 11: Guest lecture                                                          

Filip Radlinski, Microsoft Research Cambridge                                                     TITLE Abstract