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LDA-TB500 Language Technology, Module, 30 cr 
Code LDA-TB500  Validity 01.01.2017 -
Name Language Technology, Module  Abbreviation Language Techno 
Scope30 cr   
TypeAdvanced studies
TypeStudy block/Line   
  GradingGeneral scale 
Unit Master's programme in Linguistic Diversity and Digital Humanities 

Jörg Tiedemann 

Target group 







It is recommended to start the module in the second semester and finish at the end of the masters programme in year 2 (semester 4). Courses in this module will be offered during spring and autumn in year 1 and 2 of the master's programme.

Learning outcomes 

The overarching aim of the module is for the participants to gain a deep understanding of the foundations and current state of research in language technology as well as the necessary skills to design and conduct research in the field of computational linguistics and language technology.

Upon successful completion of the module, students will have the following competences:

  • Carry out research in language technology by use of experimentally oriented methods;
  • Be able to critically assess the latest literature in language technology.


  • programming for linguists (BA) or equivalent
  • mathematics for linguists (BA) or equivalent
  • machine learning for linguists (BA) or equivalent

LDA-TB500 Language Technology, Module, 30 cr


Take at least three (3) study units, minimum 15 credits in total:

LDA-T302 Computational morphology, 5 cr

LDA-T303 Computational syntax, 5 cr

LDA-T304 Computational semantics, 5 cr

LDA-T305 Models and algorithms in NLP applications, 5 cr

LDA-T306 Machine Translation, 5 cr

LDA-T307 Approaches to Natural Language Understanding, 5 cr

LDA-T308 Introduction to deep learning, 5 cr

LDA-T501 Introduction to NLP, 5 cr

Take 0-15 credits, so that 30 credits will be completed:

LDA-T309 Corpus clinic, 5 cr

LDA-T310 Research project, 5 cr

LDA-T312 Current topics in language technology, 5 cr

LDA-T313 Current topics in language technology II, 5 cr

LDA-T314 Current topics in language technology III, 5 cr

LDA-T315 Work Practice, 5 cr

LDA-P305 Speech synthesis and recognition, 5 cr

DATA11001 Introduction to data science, 5 cr

DATA11002 Introduction to machine learning, 5 cr

DATA12001 Advanced course in machine learning, 5 cr

Assessment practices and criteria 

The grade is the average of the individual courses that constitute the module.


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