{"id":17935,"date":"2021-11-23T13:41:07","date_gmt":"2021-11-23T13:41:07","guid":{"rendered":"https:\/\/www.inspirenignite.com\/up\/departmental-elective-4-kcs072-natural-language-processing-cse-7th-sem-syllabus-for-aktu-b-tech-2021-22-scheme\/"},"modified":"2021-11-23T13:41:07","modified_gmt":"2021-11-23T13:41:07","slug":"departmental-elective-4-kcs072-natural-language-processing-cse-7th-sem-syllabus-for-aktu-b-tech-2021-22-scheme","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/up\/departmental-elective-4-kcs072-natural-language-processing-cse-7th-sem-syllabus-for-aktu-b-tech-2021-22-scheme\/","title":{"rendered":"(Departmental Elective-4) KCS072: Natural language processing CSE 7th Sem Syllabus for AKTU B.Tech 2021-22 Scheme"},"content":{"rendered":"

Natural language processing detail syllabus for Computer Science Engineering (CSE), 2021-22 scheme is taken from AKTUs<\/a> 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.<\/p>\n

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

Natural language processing<\/title><\/p>\n<h4>Course Outcomes:<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" rel=\"nofollow noopener\" target=\"_blank\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" rel=\"nofollow noopener\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a> <\/p>\n<h4>Unit I<\/h4>\n<p> 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.<\/p>\n<h4>Unit II<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" rel=\"nofollow noopener\" target=\"_blank\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" rel=\"nofollow noopener\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a> <\/p>\n<h4>Unit III<\/h4>\n<p> 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.<\/p>\n<h4>Unit IV<\/h4>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" rel=\"nofollow noopener\" target=\"_blank\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" rel=\"nofollow noopener\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a> <\/p>\n<h4>Unit V<\/h4>\n<p> 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.<\/p>\n<h4>Text Books:<\/h4>\n<ol>\n<li>Daniel Jurafsky, James H. Martin’Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech, Pearson Publication, 2014.<\/li>\n<li>Steven Bird, Ewan Klein and Edward Loper, ‘Natural Language Processing with Python, First Edition, OReilly Media, 2009.<\/li>\n<li>Lawrence Rabiner And Biing-Hwang Juang, ‘Fundamentals Of Speech Recognition’, Pearson Education, 2003.<\/li>\n<li>Daniel Jurafsky And James H Martin, ‘Speech And Language Processing – An Introduction To Natural Language Processing, Computational Linguistics, And Speech Recognition’, Pearson Education, 2002.<\/li>\n<li>Frederick Jelinek, ‘Statistical Methods Of Speech Recognition’, MIT Press, 1997.<\/li>\n<li>Breck Baldwin, ‘Language Processing with Java and LingPipe Cookbook, Atlantic Publisher,<\/li>\n<li>Richard M Reese, ‘Natural Language Processing with Java, OReilly Media, 2015.<\/li>\n<li>Nitin Indurkhya and Fred J. Damerau, ‘Handbook of Natural Language Processing, Second Edition, Chapman and Hall\/CRC Press, 2010.<\/li>\n<li>Tanveer Siddiqui, U.S. Tiwary, ‘Natural Language Processing and Information Retrieval, Oxford University Press, 2008.<\/li>\n<\/ol>\n<p align=\"justify\">For the syllabus of all the subjects of B.Tech CSE 7th Sem, 2021-22 scheme do visit <a class=\"rank-math-link\" href=\"..\/category\/cse+7th-sem\">CSE 7th Sem syllabus subjects<\/a>.<\/p>\n<p id=\"istudy\" style=\"text-align:center\">For the complete syllabus, results, class timetable, and many other features kindly download the <a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy\" rel=\"nofollow noopener\" target=\"_blank\">iStudy App<\/a><br \/><b> It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.<\/b><br \/><a class=\"rank-math-link\" href=\"https:\/\/play.google.com\/store\/apps\/details?id=ini.istudy&pcampaignid=pcampaignidMKT-Other-global-all-co-prtnr-py-PartBadge-Mar2515-1\" rel=\"nofollow noopener\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/play.google.com\/intl\/en_us\/badges\/static\/images\/badges\/en_badge_web_generic.png\" alt=\"Get it on Google Play\" style=\"height:65px\"><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Natural language processing detail syllabus for Computer Science Engineering (CSE), 2021-22 scheme is taken from AKTUs official website and presented for the AKTU B.Tech students. For the course code (KCS072), […]<\/p>\n","protected":false},"author":2300,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[49],"tags":[],"class_list":["post-17935","post","type-post","status-publish","format-standard","hentry","category-cse"],"_links":{"self":[{"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/posts\/17935","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/users\/2300"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/comments?post=17935"}],"version-history":[{"count":0,"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/posts\/17935\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/media?parent=17935"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/categories?post=17935"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.inspirenignite.com\/up\/wp-json\/wp\/v2\/tags?post=17935"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}