This class provides an overview of natural language processing (NLP), its methods, and its applications. It covers both the symbolic treatment of language (formal grammars) and the sub-symbolic treatment (neural networks and transformers). The course also treats two application areas in detail: information extraction and sentiment analysis.


During this course, the students will acquire the different methods underlying speech and language processing. The techniques and concepts that will be studied include: part-of-speech tagging, information extraction, knowledge representation, dependency parsing, and application of machine learning methods (such as deep learning, hidden markov models) to text classification.