Foto: Anna Logue

Text Analytics

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. Covered topics a include a complete introduction to all major sub-fields of NLP (syntax, semantics, etc.), as well as applications (e.g., Machine Translation).

Course Details

Time and Location

  • Lecture: Tuesday 15:30 - 17:00 (at) virtual room WIM-ZOOM-05.
  • Exercise sessions: Wednesday 12:00-13:30 (at) virtual room WIM-ZOOM-05.
  • NOTE: the course starts on Tuesday 29.9!

Grading / Evaluation

  • 100% final exam

Instructors

Attendance

  • The course is open to students of the Master Business Informatics, Mannheim Master in Data Science (MMDS) and Mannheim Master in Business Research (MMBR) Information Systems.

Course materials and up-to-date information can be found in our ILIAS group.

Textbooks

D. Jurafsky, J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics (2nd ed.), Prentice-Hall, 2009.

C. Manning and H. Schütze, Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA: May 1999.