The general application requirements can be found on the web page of the LCT program. There you can also find all necessary information on application procedure and deadlines as well as the application form.
The LCT masters program at Charles University is available both for local and for non-local students.
Since LCT in Prague is strongly computationally-oriented, we require our students to have very good knowledge of computer science or mathematics. The main difference between Prague and most of the other partner universities is the amount of theoretical computer science being taught - the courses of Computability and Complexity constitute the core of the obligatory CS courses. Obviously, not only CS courses but also many LT courses presuppose good practical experience with programming both under Windows and UNIX platform.
If you have any questions concerning the program, please do not hesitate to contact us.
The aim of the study branch Computational Linguistics is to get the students ready for research in the area of natural language processing and development of applications dealing with both written and spoken language. Examples of such applications are systems of information retrieval, machine translation, grammar checking, text summarization and information extraction, automatic speech recognition, voice control, spoken dialogue systems, and speech synthesis. The emphasis is put on deep understanding of formal foundations and their practical applicability. The study branch Computational Linguistics can be studied in two specializations: (i) computational and formal linguistics, and (ii) statistical methods and machine learning in computational linguistics.
The graduate is familiar with the theoretical foundations of the formal description of natural languages, the mathematical and algorithmic foundations of automatic natural language processing, and state-of-the-art machine learning techniques. Graduates have the ability to apply the knowledge acquired during their studies in the design and development of systems automatically processing natural language and large quantities of both structured and unstructured data, such as information retrieval, question answering, summarization and information extraction, machine translation and speech processing. They are equipped with reasonable knowledge, skills, and experience in software development and teamwork applicable in all areas involving the development of applications aiding human-computer interaction and/or machine learning.