The course will have a practical focus (laboratories and practical tasks) learning to use readily available LT toolkits (Spacy, Flair, Transformers) based on machine and deep learning in a multilingual and multi-domain setting. The aim is to allow attendees to acquire the required autonomy to solve practical problems by applying and developing Language Technology applications. The course will be taught in English.
This course is targeted to graduate students and professionals from a range of disciplines (linguistics, journalism, computer science, sociology, etc.) that need an applied introduction to Language Technology. This involves identifying the required linguistic resources, appropriate tools/libraries and techniques with the aim of acquiring the required autonomy to solve practical problems by applying and developing applications based on Language Technology in different and creative ways.For the practical content (coding exercises) some experience in python programming is recommended. Previous attendance to the Deep Learning for Natural Language Processing course might be useful although not required.