Sunday, July 8, 2012

CS9040-LANGUAGE TECHNOLOGIES-B.E -CSE-COMPUTER SCIENCE AND ENGINEERING SEVENTH-VII SEMESTER 2008 REGULATION ANNA UNIVERSITY SYLLABUS



CSE Computer Science And Engineering VII-Seventh Semester Syllabus 2008 Regulation Anna University


AIM:
The aim of this course is understand the issues and challenges of tackling natural language and outline some of the techniques and heuristics used in language technologies.

OBJECTIVES:
·    To understand the issues and challenges in natural language and the various modules of a typical natural language processing system
·    To learn the indexing and searching processes of a typical  information retrieval system and to study NLP based retrieval systems
·    To gain knowledge about typical text categorization and clustering techniques
·    To know about evaluation techniques for information retrieval and text mining
·    To comprehend Multimodality and multilingualism issues
·    To gain knowledge about translation, dialog agents and Generation systems

UNIT I           INTRODUCTION                                                                                         9
Natural Language Processing Linguistic Background- Spoken language input and output Technologies Written language Input - Mathematical Methods - Statistical Modeling and Classification Finite State methods Grammar for Natural Language Processing Parsing Semantic and Logic Form Ambiguity Resolution Semantic Interpretation.

UNIT II          INFORMATION RETRIEVAL                                                                      9
Information Retrieval architecture - Indexing- Storage Compression Techniques – Retrieval Approaches Evaluation - Search engines- commercial search engine
features- comparison- performance measures Document Processing - NLP based
Information Retrieval Information Extraction.

UNIT III         TEXT MINING                                                                                              9
Categorization Extraction based Categorization- Clustering- Hierarchical Clustering- Document Classification and routing- finding and organizing answers from Text search use of categories and clusters for organising retrieval results Text Categorization and efficient Summarization using Lexical Chains Pattern Extraction (evaluation).

UNIT IV        GENERIC ISSUES                                                                                      9
Multilinguality Multilingual Information Retrieval and Speech processing - Multimodality
Text and Images – Modality Integration - Transmission and Storage Speech coding- Evaluation of systems Human Factors and user Acceptability.

UNIT V         APPLICATIONS                                                                                          9
Machine Translation Transfer Metaphor - Interlingua and Statistical Approaches - Discourse Processing Dialog and Conversational Agents Natural Language Generation Surface Realization and Discourse Planning.



TEXT BOOKS:

TOTAL: 45 PERIODS

1.  Daniel Jurafsky and James H. Martin, Speech and Language Processing , 2000.
2.  Gerald  J.  Kowalski  and  Mark.T.  Maybury,    Information  Storage  and  Retrieval systems, Kluwer academic Publishers, 2000.


REFERENCES:
1.  Tomek Strzalkowski   Natural Language Information Retrieval ,  Kluwer academic
Publishers, 1999.
2.  Christopher  D.Manning  and  Hinrich  Schutze,  Foundations  of  Statistical  Natural
Language Processing , MIT Press, 1999.
3.  Michael W. Berry Survey of Text Mining: Clustering, Classification and Retrieval”, Springer Verlag, 2003.
4.  James Allen Natural Language Understanding,  Benjamin/ Cummings Publishing
Co. 1995.

7/08/2012 02:26:00 AM

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