SAGE University, Indore
Institute Name:Institute of Engineering & Technology Department Name:Information Technology
Recommended Programs : Semeste
VII
B.Tech. Information Technology (Honours) r
Course Name Natural Language Processing Course Code ECSDCNLP001T
L T P N
Credit Hours Total Credits
3 0 0 0 3
Prerequisites 1. ECSDCDSA001T Data Structures and Algorithms
● How key concepts from NLP are used to describe and analyze language
Course ● Describe the formal language and their representation using grammars.
Objectives ● POS tagging and context free grammar for English language.
● Understanding semantics and pragmatics of English language for processing.
● Writing programs in Python to carry out natural language processing
Unit-1: Introduction (9 Hours)
Human languages, Main approach of NLP, Knowledge in speech and language processing,
Ambiguity, Models and algorithms, Formal language and Natural Language, Regular
Expression and automata.
Unit-2: Text Pre-processing (9 Hours)
Text Pre-processing, Tokenization, Feature Extraction from text, Morphology: Inflectional
and Derivational, Finite state morphological parsing, Finite state transducer
Part of Speech Tagging: Rule based, Stochastic POS, Transformation based tagging.
Unit-3: Speech Processing (9 Hours)
Course Content Speech and phonetics, Vocal organ, Phonological rules and Transducer, Probabilistic
models: Spelling error, Bayesian method to spelling, Minimum edit distance, Bayesian
method of pronunciation variation.
Unit-4: N-Grams (9 Hours)
Simple N-Gram, perplexity, Smoothing, Backoff, Entropy, Parsing: Statistical Parsing,
Probabilistic parsing, TreeBank.
Unit-5:Application (9 Hours)
Sentiment analysis, Spelling correction, Word sense disambiguation, Machine translation,
Text Classification, Question answering system
T1Daniel Jurafsky and James H. Martin, “Speech and Language Processing”, Pearson
Education.
Text Books
T2James Allen, “Natural Language Understanding”, Pearson Education.
R1Christopher D. Manning and HinrichSchutze, “Foundation of statistical Natural
Language Processing”, MIT Press.
R2Mary Dee Harris “Introduction to Natural language Processing” ,Reston
References
R3: https://nptel.ac.in/courses/nlp/
CO1.Understand natural language processing and to learn how to apply basic algorithms in
this field.
CO2.To gets acquainted with the algorithmic description of the main language levels:
morphology, syntax, semantics, and pragmatics.
Course
CO3.Understand various resources of natural language data – corpora and word net.
Outcomes
CO4.To conceives basics of knowledge representation, inference, and relations to the
artificial intelligence.
CO5.Ability to acquire knowledge of contemporary issue for Theoretical Computer
Science.
Mapping of Course outcome with Program Outcomes, PSO’s, and Knowledge Levels (As per Blooms
Taxonomy)
P P P P P P P P P Knowledge
CO/ PO PO PO PS PS PS PS
O O O O O O O O O Levels (K1, K2,
PO 10 11 12 O1 O2 O3 O4
1 2 3 4 5 6 7 8 9 …, K6)
CO1 2 1 K1,K2,K3,K4,K5,
3 3 1 2 2 K6
CO2 3 3 3 3 3 2 3 K2,K3,K4,K5
CO3 2 3 3 3 2 1 K2,K5,K6
CO4 2 3 2 3 1 2 3 3 K3,K4,K5,K6
CO5 1 2 3 3 K1,K5,K6
High-3 Medium-2 Low-1
K1 =>Remember K2 =>Understand K3 =>Apply K4 =>Analyze K5 =>Evaluate K6 =>Create
Designed By: Checked By: Approved By:
Prof. Snehlata Mishra Dr.Ritu Tandan Dr. Deepak Kumar Yadav
(Name with Sign.) (Name with Sign.) (Name with Sign.)
