Core modules ensure that the students obtain a solid common
foundation designed to cover all areas necessary for working in LCT,
including theoretical as well as practical skills;
The structure provides a unifying common axis of knowledge as well as
three specialization tracks. The specialization tracks link students with
different educational backgrounds to corresponding knowledge and skills
that are required by career paths supported by different sections of the
job market. They are characterized as follows:
• The Digital Language Resources (DLR) track equips students
having a strong linguistics background with the kind of insights and
practical skills required to design, create and exploit annotated data
resources for natural language applications or for the empirical
validation of issues in cognitive and experimental linguistics.
• The Natural Language Algorithms and Applications (NLA) track,
intended mainly for students with a computer science background, revolves
around the design and implementation of algorithms and machine learning
techniques that are relevant to fundamental natural language processing
problems such as parsing, generation, translation, as well as more
advanced applications and platforms that make use of such algorithms.
• The Language Data Science (LDS) track is aimed at students
with a strong background in computer science and mathematics, familiar
with AI approaches. It focuses on the application of Data Science
techniques to large quantities of language data of different types and
granularities in order to address important practical tasks such as
information extraction, sentiment analysis, speech recognition, data
visualisation etc.
To find out information about the master thesis, turn to the Master
Thesis page on the local Language Analysis and Processing master's
programme site. The information available there is valid for both the
local programme and the LCT programme.
