Natural Language Processing

Aim

After completion of the course, the student should on an advanced level be able to: - explain state-of-the-art natural language processing algorithms and analyse them theoretically - implement natural language processing algorithms and apply them to practical problems - design and carry out evaluations of natural language processing components and systems - seek, assess and use scientific information within the area of natural language processing

Content

Natural Language Processing (NLP) develops techniques for the analysis and interpretation of natural language, a key component of smart search engines, personal digital assistants, and many other innovative applications. The goal of this course is to provide students with a theoretical understanding of and practical experience with the advanced algorithms that power modern NLP. The course focuses on methods that involve machine learning on text data. The course covers the following areas: State-of-the-art NLP algorithms for the analysis and interpretation of words, sentences, and texts. Relevant machine learning methods based on statistical modelling, combinatorial optimisation, and neural networks. NLP applications. Validation methods. NLP tools, software libraries, and data. NLP research and development.