Introduction to Natural Language Processing

This course is not offered.

Please see for our current offerings.
If you have questions about this course, please contact the department, see Contact.



Admission requirements

1. 45 credits in Computer Science including 15 credits of programming
2. The equivalent of English B in Swedish secondary school.


Syllabus for students autumn 2014, autumn 2013

Course Code:
DA334A revision 1
Level of specialisation
Main fields of study:
No main fields
Date of establishment:
06 March 2013
Date of ratification:
18 February 2013
Decision-making body:
Faculty of Technology and Society
Enforcement date:
02 September 2013

Course description

This course provides a comprehensive introduction to the theory and practice of text-based natural language processing (NLP)—the development of computer programs that can understand, generate, translate, extract information from, and learn natural language in textual form from webpages, books, newspapers, etc.

Advancement in relation to the degree requirements

Single subject course.

Entry requirements

1. 45 credits in Computer Science including 15 credits of programming
2. The equivalent of English B in Swedish secondary school.

Learning outcomes

Knowledge and understanding
On completion of the course, the student shall:

  • demonstrate understanding of the core tasks in NLP
  • demonstrate understanding of state-of-the-art algorithms and techniques for text-based processing of natural language
  • demonstrate understanding of human languages and be familiar with the most mainstream descriptive and theoretical frameworks for handling their properties

Skills and Abilities
On completion of the course, the student shall:
  • be able to determine when a problem's complexity requires an NLP solution
  • be able to undertake the design of state-of-the-art tools for core NLP tasks


Requirements for Pass (G): Passed written examination (4 hp) and passed assignments (3.5 hp).
Requirements for Pass with distinction (VG): Majority of assignments passed with distinction and the written examination passed with distinction.

Course content

The course contains the following moments:

  • For NLP: morphology, syntax, semantics, lexical semantics
  • Formal language: regular expressions, automata, finite state transducers, context-free grammars
  • N-grams, part-of-speech tagging, hidden Markov and maximum entropy models, parsing, computational semantics, computational lexical semantics, discourse, information extraction, question answering, summarisation, machine translation

Learning activities

30 hours of lectures, 10 hours of seminars and 160 hours of independent studies.

Grading system

Fail (U), Pass (G) or Pass with Distinction (VG).

Course literature and other teaching materials

Course evaluation

All students are given the opportunity to give their comments at the end of the course in writing. A compilation of the results will be available on the faculty computer net. Student participation is in the form of course meetings.


The education is provided by the Faculty of Technology and Society at the Department of Computer Science and Media Technology.

Further information