Natural Language Processing


Friday, 4pm-6pm

Processing natural language remains one of the most difficult challenges for computers. The last few years giant leaps have been made in different language technologies through a combination of state-of-the-art speech recognition and synthesis, large scale (statistical) machine learning on enormous corpora and knowledge engineering.
This course guides the student through the different components of modern NLP software. Topics are: regular expressions, n-gram models, part-of-speech tagging, speech recognition, parsing, unification, meanings and semantics.

The evaluation consists of assignments during the semester (50%) and a final written exam (50%).