NATURAL LANGUAGE PROCESSING (BCS714B)

NATURAL LANGUAGE PROCESSING

Course Code BCS714B 
CIE Marks 50
Teaching Hours/Week (L: T:P: S) 3:0:0:0 
SEE Marks 50
Total Hours of Pedagogy 40 
Total Marks 100
Credits 03 
Exam Hours 03
Examination type (SEE) Theory

Module-1

Introduction: What is Natural Language Processing? Origins of NLP, Language and

Knowledge, The Challenges of NLP, Language and Grammar, Processing Indian Languages,

NLP Applications.

Language Modeling: Statistical Language Model - N-gram model (unigram, bigram),

Paninion Framework, Karaka theory.

Textbook 1: Ch. 1, Ch. 2.

Module-2

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 Grammar, Constituency, Top-down and Bottom-up

Parsing, CYK Parsing.

Textbook 1: Ch. 3, Ch. 4.

Module-3

Naive Bayes, Text Classification and Sentiment: Naive Bayes Classifiers, Training the

Naive Bayes Classifier, Worked Example, Optimizing for Sentiment Analysis, Naive Bayes

for Other Text Classification Tasks, Naive Bayes as a Language Model.

Textbook 2: Ch. 4.

Module-4

Information Retrieval: Design Features of Information Retrieval Systems, Information

Retrieval Models - Classical, Non-classical, Alternative Models of Information Retrieval -

Custer model, Fuzzy model, LSTM model, Major Issues in Information Retrieval.

Lexical Resources: WordNet, FrameNet, Stemmers, Parts-of-Speech Tagger, Research

Corpora.

Textbook 1: Ch. 9, Ch. 12.

Module-5

Machine Translation: Language Divergences and Typology, Machine Translation using

Encoder-Decoder, Details of the Encoder-Decoder Model, Translating in Low-Resource

Situations, MT Evaluation, Bias and Ethical Issues.

Textbook 2: Ch. 13.

Suggested Learning Resources:

Text Books:

1. Tanveer Siddiqui, U.S. Tiwary, “Natural Language Processing and Information

Retrieval”, Oxford University Press.

2. Daniel Jurafsky, James H. Martin, “Speech and Language Processing, An Introduction

to Natural Language Processing, Computational Linguistics, and Speech Recognition”,

Pearson Education, 2023.

Reference Books:

1. Akshay Kulkarni, Adarsha Shivananda, “Natural Language Processing Recipes -

Unlocking Text Data with Machine Learning and Deep Learning using Python”, Apress,

2019.

2. T V Geetha, “Understanding Natural Language Processing – Machine Learning and

Deep Learning Perspectives”, Pearson, 2024.

3. Gerald J. Kowalski and Mark.T. Maybury, “Information Storage and Retrieval systems”,

Kluwer Academic Publishers.