Text Analytics

IE 661/ IS 661 for Master Students (M.Sc. MMM, M.Sc. Wirt. Inf., MMDS, Lehramt Informatik, M. Sc. Medien- und Kommunikationswissenschaft, PhD VWL)

LecturerProf. Dr. Markus Strohmaier
Course FormatLecture
Credit Points6 ECTS
GradingWritten exam
Exam DateTo be announced
Information for StudentsPlease register in time via Portal2. Students must pass at least 50% of the written assignments in the exercise class in order to take the final exam.
Marlene Lutz, M.Sc.

Marlene Lutz, M.Sc.

For further information please contact Marlene Lutz.

Course Information

  • Course Description

    In the digital age, techniques to automatically process textual content have become ubiquitous. Given the breakneck speed at which people produce and consume textual content online – e.g., on micro-blogging and other collaborative Web platforms like wikis, forums, etc. – there is an ever-increasing need for systems that automatically understand human language, answer natural language questions, translate text, and so on. This class will provide a complete introduction to state-of-the-art principles and methods of Natural Language Processing (NLP). The main focus will be on statistical techniques, and their application to a wide variety of problems. This is because statistics and NLP are nowadays highly intertwined, since many NLP problems can be formulated as problems of statistical inference, and statistical methods, in turn, represent de-facto the standard way to solve many, if not the majority, of NLP problems.

  • Objectives

    Students will acquire knowledge of state-of-the-art principles and methods of Natural Language Processing, with a specific focus on the application of statistical methods to human language technologies.
    Successful participants will be able to understand state-of-the-art methods for Natural Language Processing, as well as being able to select, apply and evaluate the most appropriate techniques for a variety of different practical and application-oriented scenarios.

  • Requirements

    Required: -

    Recommended: Fundamental notions of linear algebra and probability theory.