Information Retrieval: 2008-2009

Information

Lecturer

Degrees

Michaelmas Term — MSc in Computer Science

Part C — Honour School of Computer Science

Part C — Honour School of Mathematics and Computer Science

MSc by Research

Term

Michaelmas Term 2008 (20 lectures)

Overview

Information Retrieval (IR), for the purpose of this course, is the study of the indexing, processing, and querying of textual data. The growing importance of the Web means that IR has acquired added significance in recent years. The course will also look at how models of language similar to those used in IR can be applied to the problem of Machine Translation (MT), which is becoming increasingly important as more and more non-English text appears on the Web.

The aim of the course is to provide an introduction to the basic principles and techniques used in IR; to demonstrate how statistical models of language can be used to solve the document retrieval problem; to consider specific IR applications such as cross-language retrieval; and to show how statistical models of language can be used to develop Machine Translation systems.

Learning outcomes

to gain an understanding of the basic concepts and techniques in Information Retrieval;
to understand how statistical models of text can be used to solve problems in IR, with a focus on how the vector-space model and the language model can be applied to the document retrieval problem;
to understand how the user can be involved in the document retrieval process, through the use of relevance feedback;
to understand how statistical models of text can be used for other IR applications, for example clustering;
to appreciate the difficulties in carrying out document retrieval on the Web, and how the hyperlink structure can facilitate accurate retrieval;
to appreciate the importance of data structures such as an index to allow efficeint access to the information in large bodies of text;
to have experience of building a document retieval system, through the practical sessions, including the implementation of a relevance feedback system;
to understand how statistical models of language can be applied to the Machine Translation problem.

Synopsis

Basics of information retrieval

Text representation and processing
Retrieval models (Boolean, vector space, language model)
Indexing
Evaluation

Advanced IR topics:

Relevance feedback - real feedback, pseudo-relevance feedback
Document and concept clustering - hierarchical clustering, k-means
Web retrieval - Page rank, difficulties of Web retrieval
Cross-language retrieval - queries in one language, documents in another
Distributional and semantic similarity - automatic thesaurus construction

Basics of statistical machine translation (MT)

Language models for MT
Estimation from parallel texts
Decoding (finding the most probable translation)

Reading list

Additional reading

Course textbook:Introduction to Information Retrieval, by Christopher Manning, Prabhakar Raghavan, and Hinrich Schutze. http://nlp.stanford.edu/IR-book/html/htmledition/irbook.html
Modern Information Retrieval (1999), by Ricardo Baeza-Yates and Berthier Ribeiro-Neto
Readings in Information Retrieval (1997), edited by Karen Sparck Jones and Peter Willett
Managing Gigabytes : Compressing and Indexing Documents and Images (1999), by Ian H. Witten, Alistair Moffat, and Timothy C. Bell.
Information Retrieval (1979), by C. J. van Rijsbergen (online at http://www.dcs.gla.ac.uk/Keith/Preface.html)