Text Mining

Aim of module

In the digital age, techniques to automatically process textual content (text mining) 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 principles and methods of Natural Language Processing (NLP). Covered topics include a complete introduction to all major sub-fields of NLP (syntax, semantics, etc.), as well as applications (e.g., Machine Translation, Information Extraction). The main focus will be on statistical techniques, and their applicationto 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.

Learning outcomes and qualification goals

Expertise: Students will acquire knowledge of state-of-the-art principles and methods of Natural Language Processing, with a specific focus on the application of statistical methods to human language technologies.

Methodological competence: Successful participants will be able to understand state-of-the-art methods for Natural Language Processing, as well as being able to select, apply and evaluate the most appropriate techniques for a variety of different practical and application-oriented scenarios.