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Information Retrieval und Extraction (351.021)


a.Univ.-Prof. Dr. Birgit Pröll (bproell(at)

Schedule and Preliminary Meeting

The course comprises 6 lectures and a final exam - for times and locations refer to KUSSS.

Exrcises have to be worked on in groups up to 3 students and presented in the first 1,5 hours of each lecture. Except for the preliminary meeting, which starts at 12:00 s.t., all lectures will start at 12:00 c.t.

Presence is obligatory at the beginning of the first course => preliminary meeting, which is meant for organisational issues, decision on course participants and group building for working on the exercises. If, for arguable reasons, your personal presence in the preliminary meeting is not possible, it is requested that a colleague assures your participation in the lecture, assignes you to a group, and circulates the information presented in the preliminary meeting; otherwise your course registration gets cancelled.

Further, presence is obligatory, when presentations of exercises are scheduled. Absence (also if excused) will imply a reduction of points of the exercises due.

Lecture Contents

- Lecture I
      Preliminary Meeting, Presence Obligatory
      Information Retrieval (IR) - Introduction, Evaluation (Recall, Precision), Indexing, Term Weighting
- Lecture II
      Information Retrieval - IR Models; Thesaurus, Classification, Relevance Feedback, Query Expansion
- Lecture III
      Information Retrieval - String Similarity, Phonetic Algorithms, Information Filtering, IR in Database Management Systems (Oracle Text), Multimedia Retrieval

- Lecture IV
      Information Extraction (IE) - Introduction, IE Types, IE Approaches (focusing on knowledge engineering approach); IE Architecture, Evaluation of IE Systems, IE Tools and Applications
- Lecture V
       Natural Language Processing (NLP)
- Lecture VI
      Question-Answering, Dialogue Systems

Course Description

Students have competence in fundamentals and technologies of
(1) information retrieval, comprising representation, storage and retrieval of textual unstructured information and of
(2) information extraction, i.e. structuring of textual data.
They are able to implement and evaluate applications in these fields and have knowledge about related fields and current research topics.


  • Fundamentals and concepts of traditional information retrieval (IR): document representation (indexing), weighting, IR models (boolsch, vector space etc.), architectures and user interfaces, evaluation of IR systems (recal, precision), advanced concepts: sting similarity, thesaurus, classification, relevance feedback, query expansion, context-based IR, IR tools and applications
  • Fundamentals and concepts of information extraction (IE): IE types, IE approaches (knowledge engineering etc.); natural language processing, architecture and user interfaces, evaluation of IE systems, IE tools and applications
  • Related areas and current topics in IR and IE: information filtering, recommender systems, multilingual IR, content based image retrieval, digital libraries, natural language user interfaces, question answering, Web search / Web mining, etc.


  • Graduation is based on the number of achieved points in the exercises and in the final exam as well as on in-class contribution. Content for the final exam includes course content as well as content presented by the students during the exercises. Both, exercises and final exam must be passed positively.


  • Slides are put on MOODLE right before the lecture.