Course - Intelligent Text Analytics and Language Understanding - TDT4310
Intelligent Text Analytics and Language Understanding
About
About the course
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
Portfolio evaluation is the basis for the grade in the course. The portfolio includes a final written exam (25%) and work (75%). 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.
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
TDT4110 ("Information Technology, Introduction") or equivalent.
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.
Subject areas
- 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
Lecturers
Department with academic responsibility
Examination
Examination
Ordinary examination - Spring 2022
Assignment/thesis
Off Campus Examination (1)
Submission 2022-06-08 Time Release 09:00
Submission 13:00 Duration 4 hours Exam system Inspera Assessment
- Other comments
- 1) Merk at eksamensform er endret som et smittevernstiltak i den pågående koronasituasjonen.