Institute of Science and Technology · Computer Science and Engineering (%30 English) · Master
Course Objective
For the purpose of this course, IR will mainly mean the study of the indexing, processing, storage and querying of textual data. The aim of the course is to provide an introduction to the core principles and techniques used in IR, and to demonstrate how statistical models of language can be used to solve document indexing and retrieval problems. In addition, we will look at the issues involved in indexing the entire web and the creative solutions to this problem currently deployed by large scale online search providers.
Course Content
Boolean Retrieval: Dictionary and postings lists, boolean querying, The term vocabulary & postings lists, Skip Pointers, Phrase Queries and Positional Indexing, Scoring, term weighting & the vector space model, Dictionaries and Tolerant Retieval, Evaluation, Relevance Feedback & Query Expansion, Probabilistic IR, Language Models for IR, Link Analysis: PageRank
Course Learning Outcomes
- Gain an understanding of the basic concepts and techniques in Information Retrieval.
- understand how statistical models of text can be used to solve problems in IR, with a focus on how the vector-space model and language models are implemented and applied to document retrieval problems
- understand how statistical models of text can be used for other IR applications, for example clustering and news aggregation
- appreciate the importance of data structures, such as an index, to allow efficient access to the information in large bodies of text
- understand common text compression algorithms and their role in the efficient building and storage of inverted indices