Information Retrieval

Paper Code: 
MCA 525A
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 
  • Introduction of IR and basic theories related to IR.
  • Standard models of IR (Boolean, Vector-space, Probabilistic and Logical models).
  • Understand the difficulty of representing and retrieving documents, images, speech, etc.
  • Be familiar with various algorithms and systems and learning concepts about web search.
8.00
Unit I: 
Introduction to IR

Motivation, Basic Concepts, Basic structure of search engine,  Past and Future, The Retrieval Process, Web search and IR,  Information Retrieval vs Data Retrieval , IR Vs. IE, Concept of relevance

13.00
Unit II: 
Indexing & Query Processing

Index Construction, Indexing techniques for textual information items, such as inverted indices,  Document Preprocessing: tokenization, stemming and stop words. Pattern Matching.

13.00
Unit III: 
Study Popular Retrieval Models

Taxonomy of Information Retrieval Models, A Formal Characterization of IR Models.

Classic Information Retrieval: Basic Concepts, Boolean Model, Vector Model, Probabilistic Model, Brief Comparison of Classic Models

 

Language modeling. Probability ranking principle. Other commonly-used techniques include relevance feedback, pseudo relevance feedback, and query expansion and its Techniques.

13.00
Unit IV: 
Retrieval Performance Evaluation

Measures to compute similarity (Cosine, Jacquard), Retrieval performance evaluation: Recall and Precision, NDCG.

13.00
Unit V: 
An Introduction to Web Search Basics

Web structure & Characteristics, Web Crawling and Indexes, Link Analysis, Introduction to IR based on Semantics, Ontologies.

ESSENTIAL READINGS: 
  • C.D. Manning, P. Raghavan, H. Schütze, “Introduction to Information Retrieval”,  Cambridge UP, 2008. 
  • D.A. Grossman, O. Frieder, “Information Retrieval: Algorithms and Heuristics”, Springer, 2004.
REFERENCES: 
  • G. Kowalski, M.T. Maybury. “Information Storage and Retrieval Systems”, Springer, 2005. 
  • C.J. van Risjbergen. “The Geometry of Information Retrieval”  Cambridge UP, 2004. 
  • B. Croft, D. Metzler, T. Strohman, “Information Retrieval in Practice” Pearson Education, 2009. 
  • R. Baeza-Yates, B. Ribeiro-Neto, “ Modern Information Retrieval” . Addison-Wesley, 1999

 

 

Academic Year: 
2018-19
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