MBIB4230 Information Retrieval
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.
Required prerequisite knowledge
The course presupposes knowledge from the bachelor courses BIB3210/BIB3220/BIB3230.
After completion of the course, 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
After completion of the course, 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
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 assessment has two parts:
A term paper and an individual six-hour school examination. The term paper can be made individually (ca. 15 pages) or in groups with 2-3 students (ca. 20 pages).
The two parts get separate marks. The term paper counts for 40 percent and the school examination counts for 60 percent of the total and final mark. The student must pass both parts to pass the course.
Students who have failed a regular examination are entitled to sit a new examination in the part(s) they have failed. If the term paper fails, the whole group need to sit the new examination.
Letter grading A-F. An internal and an external examiner make the assessment.
Examination support material
For examination under surveillance, syllabus texts and individual notes can be used.
Baeza-Yates, Ricardo & Ribeiro-Neto, Berthier . Modern information retrieval: The concepts and technology behind search . Harlow, England: Pearson Educational, 2011. Pensum: Kap. 1-9, 11-13, 16-17. Følgende sider skal leses:
- Kap 1
- Kap 2
- Kap 3 s. 57-79, 107-113 og 124-130
- Kap 4 s. 131-143 og 159-176
- Kap 5 s. 177-183 og 185-202
- Kap 6 s. 203-238
- Kap 7 s. 255-274
- Kap 8 s. 281-294 og 300-304
- Kap 9 s. 337-344
- Kap 11 s. 447-472 og 477-514
- Kap 12 s. 515-526
- Kap 13 s. 545-548
- Kap 16
- Kap 17
Belew, Richard K. Finding out about: A cognitive perspective on search engine technology and the www . - Cambridge: Cambridge University press, 2000. Pensum: Kap. 1 og 2
Datta, Ritendra et al. Image retrieval: Ideas, influences and trends of the new age. ACM computing surveys, 40(2), article 5. [Avsn. 3.2 og 3.3 leses ikke]
Fellbaum, Christine. Wordnet: An electronic lexical database . Cambridge, Mass.: MIT press, 1998. Pensum: Ch. 1, 4 og 12
Fiske, John. Kommunikationsteorier: En introduktion . [Stockholm] : Wahlström & Widstrand, 1997. Pensum: Kap. 1
Ingwersen, Peter & Järvelin, Kalervo. The turn: Integration of information seeking and retrieval in context . Dordrecht: Springer, 2005. Pensum: Kap. 2 og 6.
Wildemuth, B. M., Freund, L., & Toms, E. G. (2014). Untangling search task complexity and difficulty in the context of interactive information retrieval studies. Journal of Documentation (Festschrift in honour of Nigel Ford), 70 (6), 1118-1140.
Orio, Nicola. (2006). Music retrieval: a tutorial and review. Foundation and trends in information retrieval , 1(1).
Page, Lawrence, Sergey Brin, Rajeev Motwani & Terry Winograd. (1998). The pagerank citation ranking: bringing order to the web. 17 s. Upubl. Notat.
Ruthven, Ian & Diane Kelly. Interactive information seeking, behaviour and retrieval. London: Facet, 2011. Pensum: kap. 3
Saracevic, Tefko. (2007). Relevance: a review of the literature and a framework for thinking on the notion in information science. Part II: nature and manifestations of relevance. Journal of the American society for information science and technology . 58(13), pp. 1915-1933.
Hjørland, B. (2010) The foundation of the concept of relevance. Journal of the American society for information science and technology , 61(2) pp. 217--237
Harman, D. K. and Voorhees, E. M. (2006), TREC: An overview. Annual Review of Information Science and Technology, 40: 113–155. doi:10.1002/aris.1440400111
Jurafsky, Daniel & James H. Martin (2016). Language Modeling with N-grams. From Speech and Language Processing. Draft of November 7, 2016. Downloaded from: http://web.stanford.edu/~jurafsky/slp3/4.pdf
Denis Kotkov, Shuaiqiang Wang, Jari Veijalainen (2016). A survey of serendipity in recommender systems, Knowledge-Based Systems , Volume 111, 1 November 2016, Pages 180-192, ISSN 0950-7051, http://dx.doi.org/10.1016/j.knosys.2016.08.014.
Negre, E. (2015 ). Information and Recommender Systems , John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781119102779.fmatter