Erasmus
Mundus Master in Language
and Communication Technologies (LCT)
|
Program
| Subject | Credits | CORE |
DLR |
NLA | LDS |
|---|---|---|---|---|---|
| Programming Techniques for NLP | 6 |
✔ | |||
| Theoretical Linguistics | 6 | ✔ | |||
| NLP Applications (I) :Understanding NLP | 3 | ✔ | |||
| Machine Learning (II) | 3 | ✔ | ✔ | ||
| Corpus Linguistics | 3 | ✔ | |||
| Introduction to Machine Learning | 3 | ✔ | |||
| Computational Morphology | 4.5 | ✔ | ✔ | ||
| Computational Syntax | 4.5 | ✔ | ✔ | ||
| Computational Semantics and Pragmatics | 4.5 | ✔ | ✔ | ||
| Reasoning |
3 | ✔ | ✔ | ||
| Speech Processing | 4.5 | ✔ | ✔ | ✔ | |
| Speech Technologies | 4.5 | ✔ | ✔ | ✔ | |
| Building Language Resources | 3 | ✔ | ✔ | ||
| NLP applications
(II): Building Information Extraction, Question Answering
and Conversational Systems |
4.5 | ✔ | ✔ | ||
| Machine
Translation and Multilingualism |
4.5 | ✔ | ✔ | ||
| Language Technologies for Digital Humanities | 4.5 | ✔ | ✔ | ||
| Deep Learning | 6 | ✔ | ✔ | ||
| Research Methods for NLP | 3 | ✔ | |||
| Statistics and Mathematics for NLP | 3 | ✔ | |||
| NLP for specialized domains | 3 | ✔ | ✔ | ✔ | |
| Big Data for NLP | 3 | ✔ | ✔ |
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.
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