Course

Information Retrieval and Text Mining (DAT640)

The course offers an introduction to techniques and methods for processing, mining, and searching in massive text collections. The course considers a broad variety of applications and provides an opportunity for hands-on experimentation with state-of-the-art algorithms using existing software tools and data collections.


Dette er emnebeskrivelsen for studieåret 2025-2026

Fakta

Emnekode

DAT640

Vekting (stp)

10

Semester undervisningsstart

Autumn

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Autumn

Timeplan

Vis timeplan

Litteratur

Søk etter pensumlitteratur i Leganto

Content

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th.

  • Text preprocessing, indexing
  • Representation learning (word embeddings)
  • Text categorization
  • Search engine architecture
  • Retrieval models (vector-space model, probabilistic models, learning to rank, neural models)
  • Search engine evaluation
  • Query modeling, relevance feedback
  • Web search (link analysis)
  • Semantic search (knowledge bases, entity retrieval, entity linking)
  • Conversational information access
  • Transformers and large language models

Learning outcome

Knowledge:

  • Theory and practice of concepts, methods, and techniques for managing and analyzing large amounts of text data.

Skills:

  • Process and prepare large-scale textual data collections for retrieval and mining.
  • Apply retrieval, classification, and clustering methods to a range of information access problems.
  • Conduct performance evaluation and error analysis.

General competencies:

  • Understanding of the strengths and limitations of modern information retrieval and text mining techniques. Being able to identify promising business applications, participate in and lead such projects.

Forkunnskapskrav

Ingen

Exam

Form of assessment Weight Duration Marks Aid
Project work 1/2 Letter grades
Written exam 1/2 4 Hours Letter grades All written and printed means are allowed. Definite, basic calculator allowed, All aids are permitted - it is not permitted to collaborate / get help from other people in working with the exam task


The project is a combination of individual and group assignments. The project groups are set up by the course instructor.

There is no re-sit option on the project. If a student fails the project, they have to re-take this part next time the course is lectured.

Digital written exam.

Both assessment parts must be passed in order to achieve an overall grade in the course.

Fagperson(er)

Head of Department:

Tom Ryen

Course coordinator:

Krisztian Balog

Course teacher:

Krisztian Balog

Course teacher:

Petra Galuscakova

Method of work

6 hours of lectures/lab exercises each week.

Overlapping

Emne Reduksjon (SP)
Web Search and Data Mining (DAT630_1) , Information Retrieval and Text Mining (DAT640_1) 5

Åpent for

Admission to Single Courses at the Faculty of Science and Technology
Data Science - Master of Science Degree Programme Computer Science - Master of Science Degree Programme Industrial Automation and Signal Processing - Master's Degree Programme - 5 year
Exchange programme at Faculty of Science and Technology

Emneevaluering

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Kontakt

Head of Department:

Tom Ryen

Course coordinator:

Krisztian Balog

Course teacher:

Krisztian Balog

Course teacher:

Petra Galuscakova

Tilbys av

Faculty of Science and Technology

Department of Electrical Engineering and Computer Science

The course description is retrieved from FS (Felles studentsystem). Version 1