Information Retrieval

Lecturer: Dr K. Spärck Jones (ksj@cl.cam.ac.uk)

No. of lectures: 8

Prerequisite courses: none, although a basic encounter with Probability is assumed  

This course covers information retrieval principles and practice in the context of the development of automated methods and of the challenges presented by current mega-scale network resources and operations.

Basic problems.

Capturing information content so that documents relevant to a user's information need can be retrieved. Examples.

Key concepts and facts of life.

Indexing and indexing language, search strategy and matching function, and constraints on these. Examples.

Formal models.

Ground abstractions, quantitative (especially vector space, probabilistic) models for retrieval systems.

Implementation techniques and systems.

Term selection and weighting, term and document clustering, matching functions and relevance feedback. SMART and OKAPI systems.

Evaluation issues.

Performance criteria and effectiveness measures, test methodology, established results.

Advanced techniques and systems.

Natural language processing, multiple key types and complex matching. NYU/GE and INQUERY systems.

Present needs and opportunities.

Finding pins in haystacks, reaching multimedia dreamworlds, delegating information seeking and extraction tasks.

Recommended books:

Willett, P. (ed.) (1988). Document Retrieval Systems. London: Taylor Graham.

van Rijsbergen, C.J. (1979). Information Retrieval. London: Butterworths.

Salton, G. & McGill, M. (1983). Introduction to Modern Information Retrieval. New York: McGraw-Hill.

Frakes, W.B. & Baeza-Yates, R. (1992). Information Retrieval: Data Structures and Algorithms. Englewood Cliffs NJ: Prentice-Hall.

Spärck Jones, K. & Willett, P. (ed.) (1997). Readings in Information Retrieval. San Francisco: Morgan Kaufmann. (NB section introductions as well as selected papers.)