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