Natural Language Processing detailed Syllabus for Information Science Engineering (IS), 2018 scheme has been taken from the VTUs official website and presented for the VTU students. For Course Code, Subject Names, Teaching Department, Paper Setting Board, Theory Lectures, Tutorial, Practical/Drawing, Duration in Hours, CIE Marks, Total Marks, Credits and other information, visit full semester subjects post given below. The Syllabus PDF files can also be downloaded from the official website of the university.
For all the other VTU IS 7th Sem Syllabus for BE 2018 Scheme, visit Information Science Engineering 7th Sem 2018 Scheme.
For all the (Professional Elective-3) subjects refer to Professional Elective-3 Scheme. The detail syllabus for natural language processing is as follows.
Course Learning Objectives:
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Module – 1
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. -Textbook 1: Ch. 1,2 RBT: L1, L2, L3
Module – 2
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 GrammarConstituency- Parsing-Probabilistic Parsing. -Textbook 1: Ch. 3,4 RBT: L1, L2, L3
Module – 3
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Module – 4
Evaluating Self-Explanations in iSTART: Word Matching, Latent Semantic Analysis, and Topic Models: Introduction, iSTART: Feedback Systems, iSTART: Evaluation of Feedback Systems, Textual Signatures: Identifying Text-Types Using Latent Semantic Analysis to Measure the Cohesion of Text Structures: Introduction, Cohesion, Coh-Metrix, Approaches to Analyzing Texts, Latent Semantic Analysis, Predictions, Results of Experiments. Automatic Document Separation: A Combination of Probabilistic Classification and Finite-State Sequence Modeling: Introduction, Related Work, Data Preparation, Document Separation as a Sequence Mapping Problem, Results. Evolving Explanatory Novel Patterns for Semantically-Based Text Mining: Related Work, A Semantically Guided Model for Effective Text Mining. -Textbook 2: Ch. 6,7,8,9 RBT: L1, L2, L3
Module – 5
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. -Textbook 1: Ch. 9,12 RBT: L1, L2, L3 08
Course Outcomes:
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Question Paper Pattern:
- The question paper will have ten questions.
- There will be 2 questions from each module.
- Each question will have questions covering all the topics under a module.
- The students will have to answer 5 full questions, selecting one full question from each module.
Text Books:
- Tanveer Siddiqui, U.S. Tiwary, Natural Language Processing and Information Retrieval, Oxford University Press, 2008
- Anne Kao and Stephen R. Poteet (Eds),Natural LanguageProcessing and Text Mining, Springer-Verlag London Limited 2007
Reference Books:
For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
For the detail Syllabus of all other subjects of BE (IS) 7th Sem, visit Information Science Engineering 7th Sem Subjects.
For all (CBSE & Non-CBSC) BE/B.Tech results, visit VTU BE/B.Tech all semester results.