CS6011 NATURAL LANGUAGE PROCESSING | syllabus (ELECTIVE-V)

CS6011    NATURAL LANGUAGE PROCESSING L T P C 3 0 0 3

                                                                                          
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    (8)

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    (9)

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 (10)

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                                   (9)
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    (9)

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”, 2nd Edition,
Prentice Hall, 2008.
2. James Allen, “Natural Language Understanding”, 2nd edition, Benjamin /Cummings publishing
company, 1995.

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