East Carolina University
Department of Computer Science

CSCI 4130
Information Retrieval
Standard Syllabus

Catalog entry

P: CSCI 2540; MATH 2228 or MATH 2283. Theory and algorithms for modeling and retrieving text. Text representation, IR models, query operations, retrieval evaluation, information extraction, text classification and clustering, enterprise and Web search, and recommender systems.

Course summary

Have you ever wondered about how web search engines scour the web and find relevant documents to a query in a fraction of a second? Information Retrieval (IR) is the foundation for modern search engines.

Search is not limited to web documents. You can use IR technologies for searching digital libraries, enterprise document collections, and documents on your desktop computer. To see the power of IR in desktop search, try Spotlight Search on Mac computers.

In this course you will learn the core topics underlying the modern search technologies -- algorithms, data structures, indexing, query execution, and ranking search results. You will also learn about evaluating IR systems and developing search applications using open-source IR libraries and frameworks.

Course topics

• Document preprocessing
• Dictionary and postings lists
• Tolerant retrieval
• Index construction and compression
• Term weighting
• Set-theoretic models: Boolean, Fuzzy Set, and Extended Boolean
• Algebraic models: Vector Space, Latent Semantic Indexing, Generalized Vector Space, Neural Network
• Probabilistic models: Classical, Statistical Language Modeling, Divergence from Randomness (DFR), and Probabilistic Inference
• Axiomatic thinking for information retrieval
• Information retrieval systems evaluation
• Relevance feedback and query expansion
• Document classification and clustering
• Question-answer systems

Student learning outcomes

• Design and implement solutions to information retrieval tasks and evaluate them.
• Develop search solutions using open-source information retrieval libraries and search engines.
• Analyze performance of information retrieval systems using standard test collections.
• Make practical recommendations about deploying information retrieval systems in different search domains.

Textbook

Other required material

• Martin Kleppmann. Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Sebastopol, California: O’Reilly Media, 2017. ISBN: 978-1449373320.
• Radu Gheorghe, Matthew Lee Hinman, and Roy Russo. Elasticsearch in Action. Shelter Island, New York: Manning Publications, 2015. ISBN: 978-1617291623.
• Clinton Gormley and Zachary Tong. Elasticsearch: The Definitive Guide. A Distributed Real-Time Search and Analytics Engine. Sebastopol, California: O’Reilly Media, 2015. ISBN: 978-1449358549.
• Grant S. Ingersoll, Thomas S. Morton, and Andrew L. Farris. Taming text: how to find, organize, and manipulate it. Shelter Island, NY: Manning, 2013. ISBN: 978-1933988382.
• Dan McCreary and Ann Kelly. Making Sense of NoSQL: A guide for managers and the rest of us. Shelter Island, NY: Manning Publications, 2013. ISBN: 978-1617291074.
• Stefan Büttcher, Charles L. A. Clarke, and Gordon V. Cormack. Information retrieval: implementing and evaluating search engines. Cambridge, Massachusetts: The MIT Press, 2010. ISBN: 978-0262026512.
• W. Bruce Croft, Donald Metzler, and Trevor Strohman. Search engines: information retrieval in practice. Boston, Massachusetts: Addison-Wesley, 2010. ISBN: 978-0136072249.
• Michael McCandless, Erik Hatcher, and Otis Gospodnetic. Lucene in Action. Second. Shelter Island, New York: Manning, 2010. ISBN: 978-1933988177.
• Richard K. Belew. Finding Out About: A Cognitive Perspective on Search Engine Technology and the WWW. New York, NY: Cambridge University Press, 2001. ISBN: 978-0521630283.

Grading

Course grade is based on four components:

Grade meanings