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LDA-T3105 Modeller och algoritmer i språkteknologiska tilllämpningar, 5 sp 
Kod LDA-T3105  Giltighet 01.01.2017 -
Namn Modeller och algoritmer i språkteknologiska tilllämpningar  Förkortning Modeller och al 
Omfattning5 sp   
UndervisningsformFördjupade studier
  BedömningAllmän skala 
    Kan genomföras flera gångernej
Ansvarig enhet Magisterprogrammet i språklig diversitet och digitala metoder 

Jörg Tiedemann 


The course belongs to the MA Programme Linguistic Diversity in the Digital Age

  • study track: language technology
  • modules: Studies in Language Technology (LDA-T3100), Essentials in Language Technology (LDA-TA500), Comprehensive specialization in Language Technology (LDA-TB500)

This is an optional course.

The course is available to students from other study tracks and degree programmes.






Students are advised to take this course in year 2 (semester 3). The course is offered every year during the autumn term in period I or II.


After successfully completing the course, students will be able to

  • explain models and algorithms used in selected NLP applications
  • describe properties of local prediction models and structural prediction models and methods that can be used to train them
  • explain the differences between generative and discriminative models and between supervised and unsupervised learning
  • describe the main components of a selected NLP application, for example a machine translation system
  • train and evaluate a practical NLP model, for example a statistical or neural machine translation model
  • present experimental results in a sound and scientific manner.
Studieavsnittets form 
  • Contact teaching (lectures, tutorials, seminars)
  • Self studies and group work


  • one or more of the following: flipped classroom activities, overview paper, written exam (part I)
  • project report and presentation with peer-review (part II)


Tidigare studier eller kunskaper 
  • Programming for linguists or equivalent (BA level)
  • Mathematics for linguists or equivalent (BA level)
  • Machine learning for linguists or equivalent (BA level)
Rekommenderade valfria studier 
  • Linguistics in the digital age
  • Computational syntax
  • Computational semantics
  • Computational morphology

Part I: Models and algorithms used in common NLP applications with focus on a selected application, for example machine translation

  • common models and their components (with focus on the selected application)
  • algorithms for training, tuning and evaluating NLP applications (with focus on the selected application).

Part II: Practical project work

  • data collection
  • training and tuning models
  • evaluating and analysing.
Studiematerial och litteratur 

The literature depends on the selected application, for example Philipp Koehn: "Statistical Machine Translation" (Cambridge University Press) in case of machine translation.

Other recommended literature: Manning and Schütze: Foundations of Statistical Natural Language Processing (MIT Press).

Additional web material and literature distributed on the course.

Aktiviteter och undervisningsmetoder som stöder lärandet 
  • Lectures and tutorials
  • Interactive sessions, for example flipped classroom activities
  • Problem-based collaborative project work
  • Seminars with peer-review
  • Activities documented in Moodle
Bedömningsmetoder och kriterier 

Part I:

  • Classroom activities (presentations and discussions)
  • Written exam or term paper

Part II:

  • Individual and collaborative work
  • Project report
  • Seminar presentation and peer review.

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