CS6011 NATURAL LANGUAGE PROCESSING SYLLABUS FOR 8TH SEM CSE REGULATION 2013 - Anna University Internal marks 2018

CS6011 NATURAL LANGUAGE PROCESSING SYLLABUS FOR 8TH SEM CSE REGULATION 2013


ANNA UNIVERSITY CSE SYLLABUS
CS6011 NATURAL LANGUAGE PROCESSING SYLLABUS
8TH SEMESTER CSE
REGULATION 2013
CS6011 NATURAL LANGUAGE PROCESSING SYLLABUS
CS6011 NATURAL LANGUAGE PROCESSING SYLLABUS

OBJECTIVES:
-> The student should be made to:
-> Learn the techniques in natural language processing.
-> Be familiar with the natural language generation.
-> Be exposed to machine translation.
-> Understand the information retrieval techniques.

UNIT I OVERVIEW AND LANGUAGE MODELING
Overview: Origins and challenges of NLP-Language and Grammar-Processing Indian Languages - NLP Applications-Information Retrieval. Language Modeling: Various Grammar- based Language Models-Statistical Language Model.

UNIT II WORD LEVEL AND SYNTACTIC ANALYSIS
Word Level Analysis: Regular Expressions-Finite-State Automata-Morphological Parsing-Spelling Error Detection and correction-Words and Word classes-Part-of Speech Tagging. Syntactic Analysis: Context-free Grammar-Constituency- Parsing-Probabilistic Parsing.

UNIT III SEMANTIC ANALYSIS AND DISCOURSE PROCESSING
Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

UNIT IV NATURAL LANGUAGE GENERATION AND MACHINE TRANSLATION
Natural Language Generation: Architecture of NLG Systems- Generation Tasks and Representations- Application of NLG. Machine Translation: Problems in Machine Translation- Characteristics of Indian Languages- Machine Translation Approaches-Translation involving Indian Languages.

UNIT V INFORMATION RETRIEVAL AND LEXICAL RESOURCES
Information Retrieval: Design features of Information Retrieval Systems-Classical, Non-classical, Alternative Models of Information Retrieval – valuation Lexical Resources: World Net-Frame Net- Stemmers-POS Tagger- Research Corpora.

TOTAL: 45 PERIODS

OUTCOMES:
Upon completion of the course, the student should be able to:
-> Analyze the natural language text.
-> Generate the natural language.
-> Do machine translation.
-> Apply information retrieval techniques.

TEXT BOOK:
1. Tanveer Siddiqui, U.S. Tiwary, “Natural Language Processing and Information Retrieval”, Oxford University Press, 2008.

REFERENCES:
1. Daniel Jurafsky and James H Martin, “Speech and Language Processing: An introduction to
Natural Language Processing, Computational Linguistics and Speech Recognition”, 2 nd Edition, Prentice Hall, 2008.
2. James Allen, “Natural Language Understanding”, 2 nd edition, Benjamin /Cummings publishing company, 1995.

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