(Departmental Elective-4) KCS072: Natural language processing IT 7th Sem Syllabus for AKTU B.Tech 2021-22 Scheme

Natural language processing detail syllabus for Information Technology (IT), 2021-22 scheme is taken from AKTUs official website and presented for the AKTU B.Tech students. For the course code (KCS072), exam duration, teaching hr/week, practical hr/week, total marks, internal marks, theory marks, duration, credits, and other details do visit complete semester subjects post given below.

For the IT 7th Sem Syllabus for AKTU B.Tech 2021-22 Scheme you can visit IT 7th Sem 2021-22 Scheme. For the Departmental Elective-4 scheme of IT 7th Sem 2021-22 regulation do refer to Departmental Elective-4 IT 7th Sem scheme. The detail syllabus for natural language processing is as follows.

Natural language processing

Course Outcomes:

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Unit I

INTRODUCTION: Origins and challenges of NLP – Language Modeling: Grammar-based LM, Statistical LM – Regular Expressions, Finite-State Automata – English Morphology, Transducers for lexicon and rules, Tokenization, Detecting and Correcting Spelling Errors, Minimum Edit Distance WORD LEVEL ANALYSIS : Unsmoothed N-grams, Evaluating N-grams, Smoothing, Interpolation and Backoff – Word Classes, Part-of-Speech Tagging, Rule-based, Stochastic and Transformation-based tagging, Issues in PoS tagging – Hidden Markov and Maximum Entropy models.

Unit II

Unit III

SEMANTICS AND PRAGMATICS: Requirements for representation, First-Order Logic, Description Logics – Syntax-Driven Semantic analysis, Semantic attachments – Word Senses, Relations between Senses, Thematic Roles, selectional restrictions – Word Sense Disambiguation, WSD using Supervised, Dictionary & Thesaurus, Bootstrapping methods – Word Similarity using Thesaurus and Distributional methods.

Unit IV

Unit V

SPEECH-ANALYSIS: Features, Feature Extraction And Pattern Comparison Techniques: Speech Distortion Measures- Mathematical And Perceptual – Log-Spectral Distance, Cepstral Distances, Weighted Cepstral Distances And Filtering, Likelihood Distortions, Spectral Distortion Using A Warped Frequency Scale, LPC, PLP And MFCC Coefficients, Time Alignment And Normalization – Dynamic Time Warping, Multiple Time – Alignment Paths. SPEECH MODELING : Hidden Markov Models: Markov Processes, HMMs – Evaluation, Optimal State Sequence – Viterbi Search, Baum-Welch Parameter Re-Estimation, Implementation Issues.

Text Books:

Daniel Jurafsky, James H. Martin’Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech, Pearson Publication, 2014.
Steven Bird, Ewan Klein and Edward Loper, ‘Natural Language Processing with Python, First Edition, OReilly Media, 2009.
Lawrence Rabiner And Biing-Hwang Juang, ‘Fundamentals Of Speech Recognition’, Pearson Education, 2003.
Daniel Jurafsky And James H Martin, ‘Speech And Language Processing – An Introduction To Natural Language Processing, Computational Linguistics, And Speech Recognition’, Pearson Education, 2002.
Frederick Jelinek, ‘Statistical Methods Of Speech Recognition’, MIT Press, 1997.
Breck Baldwin, ‘Language Processing with Java and LingPipe Cookbook, Atlantic Publisher, 2015
Richard M Reese, ‘Natural Language Processing with Java, OReilly Media, 2015.
Nitin Indurkhya and Fred J. Damerau, ‘Handbook of Natural Language Processing, Second Edition, Chapman and Hall/CRC Press, 2010.
Tanveer Siddiqui, U.S. Tiwary, ‘Natural Language Processing and Information Retrieval, Oxford University Press, 2008.

For the syllabus of all the subjects of B.Tech IT 7th Sem, 2021-22 scheme do visit IT 7th Sem syllabus subjects.

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