Natural Language Processing Cse 8th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective V) detail syllabus for Computer Science & Engineering (Cse), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (CS8084), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other cse 8th sem syllabus for be 2017 regulation anna univ you can visit Cse 8th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective V subjects do refer to Professional Elective V. The detail syllabus for natural language processing is as follows.
Course Objective:
- To learn the fundamentals of natural language processing
- To understand the use of CFG and PCFG in NLP
- To understand the role of semantics of sentences and pragmatics
- To apply the NLP techniques to IR applications
Unit I
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Unit II
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 III
Syntactic Analysis
Context-Free Grammars, Grammar rules for English, Treebanks, Normal Forms for grammar – Dependency Grammar – Syntactic Parsing, Ambiguity, Dynamic Programming parsing – Shallow parsing – Probabilistic CFG, Probabilistic CYK, Probabilistic Lexicalized CFGs – Feature structures, Unification of feature structures.
Unit IV
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Unit V
Discourse Analysis and Lexical Resources
Discourse segmentation, Coherence – Reference Phenomena, Anaphora Resolution using Hobbs and Centering Algorithm – Coreference Resolution – Resources: Porter Stemmer, Lemmatizer, Penn Treebank, Brills Tagger, WordNet, PropBank, FrameNet, Brown Corpus, British National Corpus (BNC).
Course Outcome:
Upon completion of the course, the students will be able to:
- To tag a given text with basic Language features
- To design an innovative application using NLP components
- To implement a rule based system to tackle morphology/syntax of a language
- To design a tag set to be used for statistical processing for real-time applications
- To compare and contrast the use of different statistical approaches for different types of NLP applications.
Text Books:
- Daniel Jurafsky, James H. MartinSpeech 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 Pythonll, First Edition, OReilly Media, 2009.
References:
- Breck Baldwin, Language Processing with Java and LingPipe Cookbook, Atlantic Publisher, 2015.
- Richard M Reese, Natural Language Processing with Javall, 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 detail syllabus of all other subjects of BE Cse, 2017 regulation do visit Cse 8th Sem syllabus for 2017 Regulation.
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