Natural Language Processing detail syllabus for Computer Science & Engineering (CSE), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17CS741), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.
For all other cse 7th sem syllabus for be 2017 scheme vtu you can visit CSE 7th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-3 subjects do refer to Professional Elective-3. The detail syllabus for natural language processing is as follows.
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.
Module 2
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Module 3
Extracting Relations from Text: From Word Sequences to Dependency Paths: Introduction, Subsequence Kernels for Relation Extraction, A Dependency-Path Kernel for Relation Extraction and Experimental Evaluation. Mining Diagnostic Text Reports by Learning to Annotate Knowledge Roles: Introduction, Domain Knowledge and Knowledge Roles, Frame Semantics and Semantic Role Labeling, Learning to Annotate Cases with Knowledge Roles and Evaluations. A Case Study in Natural Language Based Web Search: InFact System Overview, The GlobalSecurity.org Experience.
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.
Module 5
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Course Outcomes:
The students should be able to:
- Analyze the natural language text.
- Define the importance of natural language.
- Understand the concepts Text mining.
- Illustrate information retrieval techniques.
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:
- Daniel Jurafsky and James H Martin, Speech and Language Processing: Anintroduction to Natural Language Processing, Computational Linguistics and SpeechRecognition, 2nd Edition, Prentice Hall, 2008.
- James Allen, Natural Language Understanding, 2nd edition, Benjamin/Cummingspublishing company, 1995.
- Gerald J. Kowalski and Mark.T. Maybury, Information Storage and Retrieval systems, Kluwer academic Publishers, 2000.
For detail syllabus of all other subjects of BE Cse, 2017 regulation do visit Cse 7th Sem syllabus for 2017 Regulation.
Dont forget to download iStudy for latest syllabus and results, class timetable and more.