MBIB4230 Information Retrieval Course description

This course deals with theories, methods and models for constructing, using and evaluating automatic information retrieval systems. This includes input from linguistics, mathematics, statistics and information theory.

• statistic and semantic based methods for document description and retrieval
• automatic classification and categorization
• search behaviour and how to construct systems for real users
• new methods/mediums/arenas for information retrieval, such as image and multimedia retrieval, retrieval of multilingual material etc.

The course presupposes knowledge from the bachelor courses BIB3210/BIB3220/BIB3230 or BIB3240/BIB3250/BIB3260.

None

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student has

• advanced knowledge of the theoretical fundaments for a variety of models for automatic information retrieval, and how the models can be realized with various algorithms
• advanced knowledge of user oriented views on information retrieval, both cognitive and social views, and their consequences for user interface, relevance judgements and interactivity in the retrieval process
• advanced knowledge of linked data and other semantic tools used to structure and make data available, and how to utilize such data
• thoroughly knowledge of practical experiments for evaluating information retrieval systems and models
• advanced knowledge of computational linguistics for analyzing grammar and semantics, and how this can be used in automatic information retrieval system

Skills

The student can

• participate in, and have practical experience with the development and implementing of user friendly information retrieval systems and modules
• evaluate such systems in order to obtain and use them

General competene

The student is able to

• give an account of different subdomains that use Information retrieval theories and methods
• independently design, plan, and conduct different types of algorithmic IR related studies, hereunder the choice of appropriate methods and metrics

Lectures, tasks and seminars. This includes presentation of a term paper, made individually or in groups, for discussion. Teaching will be in English when there are foreign exchange students present.

The following coursework requirements must have been approved in order for the student to take the exam:

1. Either a report on a laboratory assignment or an essay on one or more of the course subtopics, written either individually or in groups of 2-3 students, to be submitted at the end of the course. Written individually, the essay will have a scope of 15 pages, witten in group the scope will be 20 pages. For a laboratory assignment the scope will be approx. 10 pages written individually, and approx. 12 pages written in a group. The topic of the assignment is chosen in cooperation with a course teacher. The requirement also entails an oral presentation at the end of the course based on a draft. Each student is assigned an opposition of one of the other students' assignments.

All required coursework must be completed and approved by the given deadline in order for the student to take the exam. If one or more coursework requirements have not been approved, the student will be given the opportunity to submit an improved version once by the given deadline.

Individual written assignment; the student shall, within the prescribed time limit deliver an individual written 3 days home assignment of 20000 characters +/- 10 per cent. Font and font size: Arial / Calibri 12pkt. Line spacing: 1.5. The student may choose English or Norwegian as examination language.

The student may choose English or Norwegian as examination language

All examination support materials are permitted. However, sources must be stated in accordance with applicable rules for source references.

Grading scale A-F.

All exam papers are graded by one internal and one external examiner.