course-details-portlet

TDT4310 - Intelligent Text Analytics and Language Understanding

About

Examination arrangement

Examination arrangement: Portfolio assessment
Grade: Letters

Evaluation form Weighting Duration Examination aids Grade deviation
work 25/100
Written examination 75/100 4 hours D

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

Portfolio evaluation is the basis for the grade in the course. The portfolio includes a final written exam (75%) and work (25%). The results for the parts are given in %-scores, while the entire portfolio is assigned a letter grade. 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" (http://www.nltk.org/).
More detailed information will be given at the beginning of the course.

More on the course

No

Facts

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

Coursework

Term no.: 1
Teaching semester:  SPRING 2020

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
Course coordinator: Lecturer(s):

Department with academic responsibility
Department of Computer Science

Phone:

Examination

Examination arrangement: Portfolio assessment

Term Status code Evaluation form Weighting Examination aids Date Time Digital exam Room *
Spring ORD work 25/100
Room Building Number of candidates
Spring ORD Written examination 75/100 D 2020-06-02 09:00
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.
Examination

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

More on examinations at NTNU