Erasmus Mundus Master in Language
and Communication Technologies (LCT)



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Language & communication technologies
University of the Basque Country


Programming Techniques for NLP 6       1
Theoretical Linguistics  6       1
NLP Applications (I) :Understanding NLP  3       1
Research Methods for NLP  3       2
Statistics and Mathematics for NLP  3       1
Introduction to Machine Learning  3       1
Machine Learning (II)  3     1
Corpus Linguistics  3       1
Computational Morphology  4.5     1
Computational Syntax  4.5     1
Computational Semantics and Pragmatics  4.5     2
Reasoning 3     2
Speech Processing  4.5   1
Speech Technologies  4.5   2
Building Language Resources  3     1
NLP applications (II): Building Information Extraction, Question Answering and Conversational Systems 4.5     2
Machine Translation and Multilingualism 4.5     2
Language Technologies for Digital Humanities  4.5     2
Deep Learning      1

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.

To find out more information about each subject, turn to the UPV/EHU institutional website and in the Syllabus tab expand the "Programme" section

Hizkuntzaren Azterketa eta Prozesamendua