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Retrieving relevant information is one of the central activities in modern knowledge societies. The relevant information is to a large extent contained in unstructured documents, especially in textual data. The field of Information Retrieval (IR) is concerned with providing efficient access to large amounts of unstructured content: text, images, videos etc.

The following subjects will be treated:

- Basic IR Models (boolean, vector-based, probabilistic)
- Basic Indexing Techniques
- Term Weighting and Scoring
- Language models for Information Retrieval
- Relevance Feedback and Query Expansion
- Latent Semantic Indexing
- Web Search
- Evaluation of information retrieval systems

After this course the student will

- be acquainted with the main IR paradigms,
- understand how collections of items (in particular text items) can be indexed to facilitate efficient retrieval,
- understand how standard Web search engines work (using Google as an example),
- have a basic understanding of advanced IR topics such as language models, relevance feedback, latent semantic indexing, and
- have a good understanding of how IR systems are implemented

Lectures and practical exercises

Scientific papers, course slides and blackboard notes.

"Introduction to Information Retrieval", Manning, Raghavan, Schütze, Cambridge University Press, 2008 (online edition freely available)

assignments