CS6007 INFORMATION RETRIEVAL SYLLABUS FOR 7TH SEM CSE REGULATION 2013

ANNA UNIVERSITY CSE SYLLABUS

CS6007 INFORMATION RETRIEVAL SYLLABUS

7TH SEM CSE SYLLABUS

REGULATION 2013

CS6007 INFORMATION RETRIEVAL SYLLABUS

OBJECTIVES:
The Student should be made to:
-> Learn the information retrieval models.
-> Be familiar with Web Search Engine.
-> Be exposed to Link Analysis.
-> Understand Hadoop and Map Reduce.
-> Learn document text mining techniques.

 
UNIT I INTRODUCTION
Introduction -History of IR- Components of IR - Issues –Open source Search engine Frameworks - The impact of the web on IR - The role of artificial intelligence (AI) in IR – IR Versus Web Search - Components of a Search engine- Characterizing the web.

 
UNIT II INFORMATION RETRIEVAL
Boolean and vector-space retrieval models- Term weighting - TF-IDF weighting- cosine similarity – Preprocessing - Inverted indices - efficient processing with sparse vectors – Language Model based IR - Probabilistic IR –Latent Semantic Indexing - Relevance feedback and query expansion.

 
UNIT III WEB SEARCH ENGINE – INTRODUCTION AND CRAWLING
Web search overview, web structure, the user, paid placement, search engine optimization/ spam. Web size measurement - search engine optimization/spam – Web Search Architectures - crawling - meta-crawlers- Focused Crawling - web indexes –- Near-duplicate detection - Index Compression - XML retrieval.

UNIT IV WEB SEARCH – LINK ANALYSIS AND SPECIALIZED SEARCH
Link Analysis –hubs and authorities – Page Rank and HITS algorithms -Searching and Ranking – Relevance Scoring and ranking for Web – Similarity - Hadoop & Map Reduce - Evaluation -
Personalized search - Collaborative filtering and content-based recommendation of documents and products – handling “invisible” Web - Snippet generation, Summarization, Question Answering, Cross- Lingual Retrieval.

 
UNIT V DOCUMENT TEXT MINING
Information filtering; organization and relevance feedback – Text Mining -Text classification and
clustering - Categorization algorithms: naive Bayes; decision trees; and nearest neighbor -  Clustering algorithms: agglomerative clustering; k-means; expectation maximization (EM).

 
TOTAL: 45 PERIODS

 
OUTCOMES:
Upon completion of the course, students will be able to
-> Apply information retrieval models.
-> Design Web Search Engine.
-> Use Link Analysis.
-> Use Hadoop and Map Reduce.
-> Apply document text mining techniques.

 
TEXT BOOKS:
1. C. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval , Cambridge University Press, 2008.
2. Ricardo Baeza -Yates and Berthier Ribeiro - Neto, Modern Information Retrieval: The Concepts and Technology behind Search 2 nd Edition, ACM Press Books 2011.
3. Bruce Croft, Donald Metzler and Trevor Strohman, Search Engines: Information Retrieval in Practice, 1 st Edition Addison Wesley, 2009.
4. Mark Levene, An Introduction to Search Engines and Web Navigation, 2 nd Edition Wiley, 2010.

 
REFERENCES:
1. Stefan Buettcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval: Implementing and Evaluating Search Engines, The MIT Press, 2010.
2. Ophir Frieder “Information Retrieval: Algorithms and Heuristics: The Information Retrieval Series “, 2 nd Edition, Springer, 2004.
3. Manu Konchady, “Building Search Applications: Lucene, Ling Pipe”, and First Edition, Gate Mustru Publishing, 2008.