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B.Tech. IV (CO) Semester - 8

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CO426 : NATURAL LANGUAGE PROCESSING (ELECTIVE - II)

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COURSE OBJECTIVES
  • Teach students the leading trends and systems in natural language processing.
  • Make them understand the concepts of morphology, syntax, semantics and pragmatics of the language and that they are able to give the appropriate examples that will illustrate the above mentioned concepts.
  • Teach them to recognize the significance of pragmatics for natural language understanding.
  • Enable students to be capable to describe the application based on natural language processing and to show the points of syntactic, semantic and pragmatic processing.
COURSE OUTCOMES
After successful completion of this course, student will be able to
  • Understand approaches to syntax and semantics in NLP.
  • Understand approaches to discourse, generation, dialogue and summarization within NLP.
  • Understand current methods for statistical approaches to machine translation.
  • Understand machine learning techniques used in NLP, including hidden Markov models and probabilistic context-free grammars, clustering and unsupervised methods, log-linear and discriminative models, and the EM algorithm as applied within NLP
COURSE CONTENT
  • INTRODUCTION

    • (02 Hours)
  • WORDS

    • (08 Hours)

    Regular Expressions and Automata, Finite State Transducers and Morphology, Probabilistic models of spelling, N-grams
  • SYNTAX

    • (10 Hours)

    Word Classes and Part of Speech Tagging using Maximum Entropy models and Hidden Markov Models, Context Free Grammars for English, Features and Unification, Lexicalized and Probabilistic Parsing, Language and Complexity
  • SEMANTICS

    • (10 Hours)

    Representing Meaning, Semantic Analysis, Lexical Semantics, Word Sense Disambiguation and Information Retrieval
  • PRAGMATICS

    • (08 Hours)

    Discourse, Dialogue and Conversational agents, Natural Language Generation, Machine Translation
  • ADVANCED TOPICS

    • (04 Hours)
  • Tutorials will be based on the coverage of the above topics separately

    • (14 Hours)

    (Total Contact Time: 42 Hours + 14 Hours = 56 Hours)

    BOOKS RECOMMENDED

    1. Daniel Jurafsky, James H. Martin: "Speech and Language Processing", 2/E, Prentice Hall, 2008.
    2. James Allen, "Natural Language Understanding", 2/E, Addison-Wesley, 1994
    3. Christopher D. Manning, Hinrich Schutze: "Foundations of Statistical Natural Language Processing", MIT Press, 1999
    4. Steven Bird, Natural Language Processing with Python, 1st Edition, O'Reilly, 2009.
    5. Jacob Perkins, Python Text Processing with NLTK 2.0 Cookbook, Packt Publishing, 2010.