Syllabus for Natural Language Processing


A revised version of the syllabus is available.


  • 15 credits
  • Course code: 5LN710
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Language Technology A1N
  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2012-03-16
  • Established by: The Department Board
  • Applies from: Spring 2012
  • Entry requirements:

    Bachelor's degree and at least 60 credits in language engineering/computational linguistics; or at least 60 credits in computer science; or at least 60 credits in some linguistic subject in addition to 15 credits in programming and 7.5 credits logic/discrete mathematics. Knowledge in English equivalent what is required for general entry requirements to Swedish first-cycle courses and study programmes.

  • Responsible department: Department of Linguistics and Philology

Decisions and guidelines

The course is given within the Master's programme in language engineering and as a freestanding course.

Learning outcomes

On completion of the course, the student should to deserve the grade Pass at least be able to:

  1. account for which products, employments and technologies that are typical for the field language engineering at a general level account for the technology behind some important systems and account for these the performance of system and commercial importance;
  2. account for central methodological considerations behind collection, selection and using linguistic data within language engineering;
  3. apply and account for basic probability theory and principles of statistical inference on such data and apply principles of maximiskattning (expectation-maximisation) on models with hidden variables;
  4. account for and apply statistical language models and different methods for regularisation;
  5. implement simple statistical models of classification and marking of symbolsekvenser particularly in system for morphological analysis and tagging of natural language, and evaluate such systems;
  6. account for and implement algorithms for syntactic parsing and disambiguation and adapt and evaluate such systems for selected languages;
  7. account for and implement language engineering methods as prisoners and/or classify the contents of texts on natural language, and account for how such systems can be evaluated;
  8. present the results of these types of language engineering tasks on a professional adequate way both orally and in writing.


The teaching is given as teaching sessions and laboratory sessions under supervision.


Examination takes place through orally and/or in writing presented laboratory assignments and/or written tests. The teacher can as part of the examination require compulsory attendance and active participation in teaching parts. The forms for examination are informed in writing of responsible at the start of the course.

Reading list

Reading list

Applies from: Spring 2012

Some titles may be available electronically through the University library.

  • Jurafsky, Dan; Martin, James H. Speech and language processing : an introduction to natural language processing, computational linguistics and speech recognition

    2. ed.: Upper Saddle River, N.J.: Pearson Education International/Prentice Hall, cop. 2009

    Find in the library


Last modified: 2022-04-26