23 June 2015

Information Retrieval Systems

Objectives:
On completion of this course you should have gained a good understanding of the foundation concepts of information retrieval techniques and be able to apply concepts into practice. Specifically, you should be able:
> To use different information retrieval techniques in various application areas
> To apply IR principles to locate relevant information large collections of data
> To analyze performance of retrieval systems when dealing with unmanaged data sources
> To implement retrieval systems for web search tasks.


UNIT I
Boolean retrieval. The term vocabulary and postings lists. Dictionaries and tolerant retrieval. Index construction. Index compression.

UNIT II
Scoring, term weighting and the vector space model. Computing scores in a complete search system. Evaluation in information retrieval. Relevance feedback and query expansion.

UNIT III
XML retrieval. Probabilistic information retrieval. Language models for information retrieval. Text classification. Vector space classification.

UNIT IV
Support vector machines and machine learning on documents, Flat clustering, Hierarchical clustering, Matrix decompositions and latent semantic indexing.

UNIT V
Web search basics. Web crawling and indexes, Link analysis.

TEXT BOOK:
1. Introduction to Information Retrieval, Christopher D. Manning and Prabhakar Raghavan and Hinrich Sch├╝tze, Cambridge University Press, 2008.

REFERENCE BOOKS:
1. Information Storage and Retrieval Systems: Theory and Implementation, Kowalski, Gerald, Mark T Maybury, Springer.
2. Modern Information Retrieval, Ricardo Baeza-Yates, Pearson Education, 2007.
3. Information Retrieval: Algorithms and Heuristics, David A Grossman and Ophir Frieder, 2nd Edition, Springer, 2004.
4. Information Retrieval Data Structures and Algorithms, William B Frakes, Ricardo Baeza-Yates, Pearson Education, 1992.
5. Information Storage & Retrieval, Robert Korfhage, John Wiley & Sons.

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