ANNA UNIVERSITY, CHENNAI
REGULATIONS - 2013
CP7024 INFORMATION RETRIEVAL TECHNIQUES SYLLABUS
CP7024 INFORMATION RETRIEVAL TECHNIQUES SYLLABUS
ME 3RD SEM COMPUTER SCIENCE AND ENGINEERING SYLLABUS
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| CP7024 INFORMATION RETRIEVAL TECHNIQUES SYLLABUS |
OBJECTIVES:
To understand the basics of Information Retrieval with pertinence to modeling, query operations and indexing
To get an understanding of machine learning techniques for text classification and clustering
To understand the various applications of Information Retrieval giving emphasis to Multimedia IR, Web Search
To understand the concepts of digital libraries
UNIT I INTRODUCTION
Motivation – Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean Retrieval –Retrieval Evaluation – Open Source IR Systems–History of Web Search – Web Characteristics–The impact of the web on IR ––IR Versus Web Search–Components of a Search engine
UNIT II MODELING
Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term Weighting – Scoring and Ranking –Language Models – Set Theoretic Models - Probabilistic Models – Algebraic Models – Structured Text Retrieval Models – Models for Browsing
UNIT III INDEXING
Static and Dynamic Inverted Indices – Index Construction and Index Compression Searching - Sequential Searching and Pattern Matching. Query Operations -Query Languages–Query Processing - Relevance Feedback and Query Expansion - Automatic Local and Global Analysis – Measuring Effectiveness and Efficiency.
UNIT IV CLASSIFICATION AND CLUSTERING
Text Classification and Naïve Bayes – Vector Space Classification – Support vector machines and Machine learning on documents. Flat Clustering – Hierarchical Clustering –Matrix decompositions and latent semantic indexing – Fusion and Meta learning
UNIT V SEARCHING AND RANKING
Searching the Web –Structure of the Web –IR and web search – Static and Dynamic Ranking - Web Crawling and Indexing – Link Analysis - XML Retrieval Multimedia IR: Models and Languages – Indexing and Searching Parallel and Distributed IR – Digital Libraries
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
Build an Information Retrieval system using the available tools
Identify and design the various components of an Information Retrieval system
Apply machine learning techniques to text classification and clustering which is used for efficient Information Retrieval
Analyze the Web content structure
Design an efficient search engine
REFERENCES:
1. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval: The concepts and Technology behind Search (ACM Press Books), Second Edition 2011
2. Christopher D. Manning, PrabhakarRaghavan, HinrichSchutze, Introduction to Information Retrieval, Cambridge University Press, First South Asian Edition 2012
3. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval Implementing and Evaluating Search Engines, The MIT Press, Cambridge, Massachusetts London, England, 2010
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