TDT4310 - Intelligent Text Analytics and Language Understanding


Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
Approved exercises 0/100
Home examination 25/100 4 hours
work 75/100

Course content

The subject comprises: human languages and language processing, machine learning and statistical methods for language understanding and text analytics, sentiment analysis and opinion mining, information access and text mining, conversational agents, machine translation, computational semantics, computational linguistic creativity.

Learning outcome

Students should have a basic and hands-on understanding of the currently used frameworks and methods for text analytics and natural language understanding, in particular the application of machine learning methods to text analytics.

Learning methods and activities

Lectures and exercises. The subject requires an approved project report with theoretical and experimental content. The lectures will be given in English, unless all students speak Norwegian.

Further on evaluation

Folder assessment, with three parts:
Exercises, compulsory (0% of the grade)

Project: 75% of the grade (total for report and presentation)

Exam: 25% of the grade (home exam on a regular exam day)

Letter grades (A-F).


Students must have:

-completed all (6) exercises

-Passed on (home) exam 25% (must get at least 53% on the exam, ie equivalent to D to be able to pass the whole course when the exam is only 25% of the grade)

-approved project 75% (report, implementation and presentation). to get the topic approved.


If there is a re-sit examination, the examination form may change from written to oral.

In the case that the student receives an F/Fail as a final grade after both ordinary and re-sit exam, then the student must retake the course in its entirety. Submitted work that counts towards the final grade will also have to be retaken.

Course materials

* "Applied Text Analysis with Python" by Benjamin Bengfort, Tony Ojeda and Rebecca Bilbro (O'Reilly Media, June, 2018).
* "Natural Language Toolkit" (
More detailed information will be given at the beginning of the course.

More on the course

Version: 1
Credits:  7.5 SP
Study level: Second degree level


Term no.: 1
Teaching semester:  SPRING 2021

No.of lecture hours: 2
Lab hours: 8
No.of specialization hours: 2

Language of instruction: English, Norwegian

Location: Trondheim

Subject area(s)
  • Computer and Information Science
  • Applied Information and Communication Technology
  • Computer Science
  • Knowledge Systems
  • Information Systems
  • Computer Systems
  • Applied Linguistics
  • Applied Linguistics
  • Information Technology and Informatics
  • Communication and Information Science
  • Languages, Logics and Inform. Technology
Contact information

Department with academic responsibility
Department of Computer Science



Examination arrangement: Portfolio assessment

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Summer UTS Approved exercises 0/100
Room Building Number of candidates
Spring ORD Approved exercises 0/100
Room Building Number of candidates
Summer UTS Home examination 25/100 INSPERA
Room Building Number of candidates
Spring ORD Home examination 25/100

Release 2021-05-27

Submission 2021-05-27

Release 09:00

Submission 13:00

Room Building Number of candidates
Summer UTS work 75/100
Room Building Number of candidates
Spring ORD work 75/100
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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