DLO8012: Natural Language Processing Syllabus for CS 8th Sem 2019 Pattern Mumbai University (Department Level Optional Course-4)

Natural Language Processing detailed syllabus scheme for Computer Engineering (CS), 2019 regulation has been taken from the MU official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.

For 8th Sem Scheme of Computer Engineering (CS), 2019 Pattern, do visit CS 8th Sem Scheme, 2019 Pattern. For the Department Level Optional Course-4 scheme of 8th Sem 2019 regulation, refer to CS 8th Sem Department Level Optional Course-4 Scheme 2019 Pattern. The detail syllabus for natural language processing is as follows.

Natural Language Processing Syllabus for Computer Engineering BE 8th Sem 2019 Pattern Mumbai University

Course Objectives:

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Course Outcomes:

On successful completion of course learner should:

1. Have a broad understanding of the field of natural language processing.
2. Have a sense of the capabilities and limitations of current natural language technologies,
3. Be able to model linguistic phenomena with formal grammars.
4. Be able to Design, implement and test algorithms for NLP problems
5. Understand the mathematical and linguistic foundations underlying approaches to the various areas in NLP
6. Be able to apply NLP techniques to design real world NLP applications such as machine translation, text categorization, text summarization, information extraction..etc.

Prerequisites:

Data structure & Algorithms, Theory of computer science, Probability Theory.

Module 1

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Module 2

Word Level Analysis Morphology analysis -survey of English Morphology, Inflectional morphology & Derivational morphology, Lemmatization, Regular expression, finite automata, finite state transducers (FST) ,Morphological parsing with FST , Lexicon free FST Porter stemmer. N -Grams- N-gram language model, N-gram for spelling correction. 10

Module 3

Syntax analysis Part-Of-Speech tagging( POS)- Tag set for English ( Penn Treebank ) , Rule based POS tagging, Stochastic POS tagging, Issues -Multiple tags & words, Unknown words. Introduction to CFG, Sequence labeling: Hidden Markov Model (HMM), Maximum Entropy, and Conditional Random Field (CRF). 10

Module 4

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Module 5

Pragmatics Discourse -reference resolution, reference phenomenon , syntactic & semantic constraints on co reference 8

Module 6

Applications ( preferably for Indian regional languages. Machine translation, Information retrieval, Question answers system, categorization, summarization, sentiment analysis, Named Entity Recognition. 10

Text 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.
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Reference Books:

1. Siddiqui and Tiwary U.S., Natural Language Processing and Information Retrieval, Oxford University Press (2008).
2. Daniel M Bikel and Imed Zitouni Multilingual natural language processing applications Pearson, 2013
3. Alexander Clark (Editor), Chris Fox (Editor), Shalom Lappin (Editor) The Handbook of Computational Linguistics and Natural Language Processing ISBN: 978-1-118-
4. Steven Bird, Ewan Klein, Natural Language Processing with Python, OReilly
5. Brian Neil Levine, An Introduction to R Programming
6. Niel J le Roux, Sugnet Lubbe, A step by step tutorial : An introduction into R application and programming

Assessment

Internal Assessment: Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when approx. 40% syllabus is completed and second class test when additional 40% syllabus is completed. Duration of each test shall be one hour. End Semester Theory Examination:

1. Question paper will comprise of 6 questions, each carrying 20 marks.
2. The students need to solve total 4 questions.
3. Question No.1 will be compulsory and based on entire syllabus.
4. Remaining question (Q.2 to Q.6) will be selected from all the modules.

Description: The Laboratory Work (Experiments) for this course is required to be performed and to be evaluated in Computational Lab-II The objective of Natural Language Processing lab is to introduce the students with the basics of NLP which will empower them for developing advanced NLP tools and solving practical problems in this field.

Reference Books:

for Experiments: http://cse24-iiith.virtual-labs.ac.in/#

Reference Books:

for NPTEL: http://www.cse.iitb.ac.in/~cs626-449 Sample Experiments: possible tools / language: R tool/ Python programming Language

Note: Although it is not mandatory, the experiments can be conducted with reference to any Indian regional language.

1. Preprocessing of text (Tokenization, Filtration, Script Validation, Stop Word Removal, Stemming)
2. Morphological Analysis
3. N-gram model
4. POS tagging
5. Chunking
6. Named Entity Recognition
7. Case Study/ Mini Project based on Application mentioned in Module 6

For detail Syllabus of all subjects of Computer Engineering (CS) 8th Sem 2019 regulation, visit CS 8th Sem Subjects of 2019 Pattern.