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Computer Science Tripos Syllabus - Information Retrieval
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Information Retrieval

Lecturer: Dr S.H. Teufel

No. of lectures: 8

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


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


  • 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.


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 recognize the limits of these strategies

  • understand (the reasons for) the evaluation strategies developed for these three areas

Recommended books

* Baeza-Yates, R. & Ribiero-Neto, B. (1999). Modern information retrieval. Reading, MA: Addison-Wesley and ACM Press.
* Salton, G. & McGill, M. (1983). Introduction to modern information retrieval. New York: McGraw Hill.
Spärck Jones, K. & Willett, P. (eds.) (1997). Readings in information retrieval. San Francisco: Morgan Kaufmann.

next up previous contents
Next: Natural Language Processing Up: Lent Term 2005: Part Previous: Computer Vision   Contents
Christine Northeast
Wed Sep 8 11:57:14 BST 2004