Natural Language Processing


Friday, 2pm-4pm (PL9.3.31)

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 range from basic text processing techniques such as regular expressions, n-gram models and word embeddings to more advanced capita selecta in natural language understanding (semantic parsing, information extraction, question answering, ...).

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