Course - Intelligent Text Analytics and Language Understanding - TDT4310
TDT4310 - Intelligent Text Analytics and Language Understanding
About
Examination arrangement
Examination arrangement: Aggregate score
Grade: Letter grades
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Assignment | 75/100 | |||
| School exam | 25/100 | 4 hours | D |
Course content
The subject comprises: human languages and language processing, machine learning and statistical methods for language understanding and text analytics, text classification and deep learning, 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.
Compulsory assignments
- Assignments
Further on evaluation
A written exam (25%) and project work (75%) form the basis for the final grade in the course. The parts and the entire course are assigned letter grades. If there is a re-sit examination, the examination form may change from written to oral. Exercises from earlier may be approved by agreement with the course coordinator.
Specific conditions
Compulsory activities from previous semester may be approved by the department.
Recommended previous knowledge
The course is open to all students, but presupposes a knowledge of Python programming at least at the level of TDT4110 ("Information Technology, Introduction"); a programming knowledge at the level of a 3rd year Computer Science student is recommend. Basic knowledge of machine learning is not required, but recommended. No knowledge of Linguistics or of language processing is assumed.
Required previous knowledge
Python programming knowledge at the level of TDT4110 ("Information Technology, Introduction") or equivalent.
Course materials
Articles, handouts and online books. More detailed information will be given at the beginning of the course.
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2023
Language of instruction: English, Norwegian
Location: Trondheim
- 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
Department with academic responsibility
Department of Computer Science
Examination
Examination arrangement: Aggregate score
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Spring ORD School exam 25/100 D 2023-05-30 09:00 PAPIR
-
Room Building Number of candidates Storhall del 2 Idrettssenteret (Dragvoll) 49 DI42 Idrettssenteret (Dragvoll) 1 -
Spring
ORD
Assignment
75/100
Submission
2023-05-30
14:00 -
Room Building Number of candidates - Summer UTS School exam 25/100 D
-
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"