Computer Laboratory – Course material 2009

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Information Retrieval

Lecturer: Dr S. Clark

No. of lectures: 8

Prerequisite courses: Natural Language Processing, and a basic encounter with Probability is assumed

Aims

The course is aimed to characterise information retrieval in terms of the data, problems and concepts involved. The main formal retrieval model and the main evaluation methods are described. The course then covers problems and standard solutions in information extraction, and in question answering.

Lectures

Information retrieval introduction. Key problems and concepts. Information need. Indexing model. Examples.
Retrieval models. Boolean model. Vector Space model. Stemming.
Evaluation methodology. TREC. User experiments. Evaluation metrics.
Search engines and linkage algorithms. PageRank and Kleinberg's Hubs and Authorities.
Information extraction. Task and evaluation. Lexico-semantic patterns.
Advanced information extraction methods. Bootstrapping. Learning.
Question answering. Performance criteria and effectiveness measures, test methodology, established results.
Overview of summarisation technology. Extractive versus abstractive summarisation. Evaluation.

Objectives

At the end of this course, students should be able to

define the tasks of information retrieval, question answering and information extraction and differences between them;
understand the main concepts and strategies used in IR, QA, and IE;
appreciate the challenges in these three areas;
develop strategies suited for specific retrieval, extraction or question situations, and recognise the limits of these strategies;
understand (the reasons for) the evaluation strategies developed for these three areas.

Recommended reading

* Manning, C.D., Raghavan, P. & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press. Available at http://www-csli.stanford.edu/~hinrich/information-retrieval-book.html.

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