PE404-Information-Retrieval-Techniques.org

<<<PE404>>> INFORMATION RETRIEVAL TECHNIQUES

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L	T	P	C
3	0	0	3

CO PO MAPPING

		PO1	PO2	PO3	PO4	PO5	PO6	PO7	PO8	PO9	PO10	PO11	PO12	PSO1	PSO2	PSO3
		K3	K4	K5	K5	K6	-	-	-	-	-	-	-	K5	K3	K6
CO1	K3	3	2	2	0	1	0	0	1	1	1	0	1	2	3	1
CO2	K3	3	2	2	0	1	0	0	1	1	1	0	1	2	3	1
CO3	K3	3	2	2	0	1	0	0	1	1	1	0	1	2	3	1
CO4	K2	2	1	1	0	1	0	0	1	1	1	0	1	1	2	0
CO5	K3	3	2	2	0	1	0	0	1	1	1	0	1	2	3	1
Score		15	11	10	0	6	0	0	5	5	5	0	5	10	15	6
Course Mapping		3	3	2	0	2	0	0	1	1	1	0	1	2	3	2

COURSE OBJECTIVES

• To understand the basics of information retrieval with pertinence to modeling
• To understand various components of IR system
• To understand machine learning techniques for text classification and clustering
• To explore various IR applications.

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UNIT I

INTRODUCTION AND MODELING

9

Basic Concepts: Retrieval process – Architecture – Boolean retrieval; IR Models: Taxonomy and characterization of IR models – Classical IR models – Alternative algebraic models – Models for Browsing – Retrieval Evaluation: Performance evaluation.

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UNIT II

INDEXING AND QUERYING

9

Indexing: Inverted indices – Suffix trees – Suffix arrays – Compression; Querying: Query languages; Query Operations: Relevance feedback and query expansion – Automatic local and global analysis.

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UNIT III

SEARCHING

9

Searching: Sequential searching – Pattern matching; Searching the Web: Characteristizing the Web – Search engines – Browsing – Searching using hyperlinks.

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UNIT IV

CLASSIFICATION AND CLUSTERING

9

Text Classification: Naive Bayes; Vector Space Classification: Rocchio – k-Nearest Neighbour; Flat Clustering: K-Means – Model-based clustering – Hierarchical clustering – Matrix decompositions and latent semantic indexing.

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UNIT V

APPLICATIONS

9

XML Retrieval – Multimedia IR – Parallel and Distributed IR – Digital Libraries – Social Media Retrieval – Content-based Image Retrieval – Online Public Access Catalogs (OPACs).

\hfill Total Periods: 45

COURSE OUTCOMES

After the completion of this course, students will be able to:

• Describe various IR modeling techniques (K3)
• Identify and design the various components of an Information Retrieval system (K3)
• Apply machine learning techniques to text classification and clustering for efficient Information Retrieval (K3)
• Describe various IR applications (K2)
• Apply IR evaluation metrics to measure the performance of IR systems (K3).

TEXT BOOKS

1. Ricardo Baeza Yates, Berthier Ribeiro Neto, “Modern Information Retrieval: The Concepts and Technology behind Search”, ACM Press Books, 2nd Edition, 2011.
2. Christopher D Manning, Prabhakar Raghavan, Hinrich Schutze, “Introduction to Information Retrieval”, Cambridge University Press, 1st South Asian Edition, 2008.

REFERENCES

1. Stefan Buttcher, Charles L A Clarke, Gordon V Cormack,“Information Retrieval – Implementing and Evaluating Search Engines”, The MIT Press, Cambridge, Massachusetts London, England, 2010.
2. Cheng Xiang Zhai, Sean Massung, “Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining”, ACM Books, 2016.
3. Reza Zafarani, Mohammad Ali Abbasi, Huan Liu, “Social Media Mining: An Introduction”, 1st Edition, Cambridge University Press, 2014.
4. Vipin Tyagi, “Content-Based Image Retrieval: Ideas, Influences, and Current Trends”, 1st Edition, Springer, 2017.
5. Marcia J Bates, “Understanding Information Retrieval Systems: Management, Types, and Standards”, CRC Press, 2012.