BCAI052 Natural Language Processing Syllabus
AKTU B.Tech · 3rd Year · Unit-wise syllabus (effective 2024-25)
BCAI052 Natural Language Processing (also written as KCAI052) is a subject in the 3rd year of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University with an L-T-P of 3-0-0. The syllabus is divided into 5 units: INTRODUCTION, SYNTACTIC ANALYSIS and Feature Systems and Augmented Grammars and Ambiguity Resolution, SEMANTICS AND PRAGMATICS, BASIC CONCEPTS of Speech Processing, SPEECH-ANALYSIS. The complete unit-wise topic list, course outcomes and reference books are given below, taken from the official AKTU syllabus effective from 2024-25.
BCAI052 Subject Details
| Subject Code | BCAI052 / KCAI052 |
|---|---|
| Subject Name | Natural Language Processing |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year |
| L-T-P | 3-0-0 |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-19 |
BCAI052 Natural Language Processing Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1: INTRODUCTION
INTRODUCTION: Origins and challenges of NLP – Language Modeling: Grammar-based LM, Statistical LM – Regular Expressions, Finite-State Automata – English Morphology, Transducers for lexicon and rules, Tokenization, Detecting and Correcting Spelling Errors, Minimum Edit Distance WORD LEVEL ANALYSIS: Unsmoothed N-grams, Evaluating N-grams, Smoothing, Interpolation and Backoff – Word Classes, Word Tokenization, Math with words TF-IDF Vectors, Finding meaning in word count (Semantic Analysis), Linguistic Background: Outline of English Syntax, Introduction to Semantics and Knowledge Representation, Zipf’s Law
Unit 2: SYNTACTIC ANALYSIS · Feature Systems and Augmented Grammars · Ambiguity Resolution
SYNTACTIC ANALYSIS: Context Free Grammars, Grammar rules for English, Grammars and Parsing: Grammars and sentence Structure, Top-Down and Bottom-Up Parsers, Transition Network Grammars, Top- Down Chart Parsing. Feature Systems and Augmented Grammars: Basic Feature system for English, Morphological Analysis and the Lexicon, Parsing with Features, Augmented Transition Networks. Ambiguity Resolution: Statistical Methods, Probabilistic Language Processing, Estimating Probabilities, Part-of-Speech tagging, Obtaining Lexical Probabilities, Probabilistic Context-Free Grammars, Best First Parsing Feature structures, Unification of feature structures.
Unit 3: SEMANTICS AND PRAGMATICS
SEMANTICS AND PRAGMATICS: Requirements for representation, First-Order Logic, Description Logics – Syntax-Driven Semantic analysis, Semantic attachments – Word Senses, Relations between Senses, Thematic Roles, selectional restrictions – Word Sense Disambiguation, WSD using Supervised, Dictionary & Thesaurus, Bootstrapping methods – Word Similarity using Thesaurus and Distributional methods.
Unit 4: BASIC CONCEPTS of Speech Processing
BASIC CONCEPTS of Speech Processing: Speech Fundamentals: Articulatory Phonetics – Production And Classification Of Speech Sounds; Acoustic Phonetics – Acoustics Of Speech Production; Review Of Digital Signal Processing Concepts; Short-Time Fourier Transform, FilterBank And LPC Methods.
Unit 5: SPEECH-ANALYSIS
SPEECH-ANALYSIS: Features, Feature Extraction And Pattern Comparison Techniques: Speech Distortion Measures– Mathematical And Perceptual Real World NLP Challenges-Information Extraction and Question Answering, Dialog Engines, Optimization, Parallelization and batch processing.
BCAI052 Course Outcomes
- At the end of course, the student will be able:
- CO 1 To learn the fundamentals of natural language processing K1, K2
- CO 2 To understand the use of CFG and PCFG in NLP K1, K2
- CO 3 To understand the role of semantics of sentences and pragmatic K2
- CO 4 K1, K2
- To Introduce Speech Production And Related Parameters Of Speech. K3, K4
- To Show The Computation And Use Of Techniques Such As Short Time Fourier Transform,
- Linear Predictive Coefficients And Other Coefficients In The Analysis Of Speech.
BCAI052 Reference Books
- Daniel Jurafsky, James H. Martin―Speech and Language Processing: An Introduction to Natural Language
- Steven Bird, Ewan Klein and Edward Loper, ―Natural Language Processing with Python, First Edition, OReilly
- Lawrence Rabiner And Biing-Hwang Juang, “Fundamentals Of Speech Recognition”, Pearson Education, 2003.
- Daniel Jurafsky And James H Martin, “Speech And Language Processing – An Introduction To Natural Language
- Frederick Jelinek, “Statistical Methods Of Speech Recognition”, MIT Press, 1997.
- 1. Breck Baldwin, ―Language Processing with Java and LingPipe Cookbook, Atlantic Publisher, 2015.
- Richard M Reese, ―Natural Language Processing with Java, OReilly Media, 2015.
- Nitin Indurkhya and Fred J. Damerau, ―Handbook of Natural Language Processing, Second Edition, Chapman and
- Tanveer Siddiqui, U.S. Tiwary, ―Natural Language Processing and Information Retrieval, Oxford University Press,
Frequently Asked Questions about BCAI052
What is the syllabus of BCAI052 Natural Language Processing?
The AKTU BCAI052 Natural Language Processing syllabus is divided into 5 units: INTRODUCTION; SYNTACTIC ANALYSIS and Feature Systems and Augmented Grammars and Ambiguity Resolution; SEMANTICS AND PRAGMATICS; BASIC CONCEPTS of Speech Processing; SPEECH-ANALYSIS. The complete topic list for each unit is published on this page, taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus effective from 2024-25.
How many units are there in BCAI052?
BCAI052 Natural Language Processing has 5 units in the AKTU syllabus, with an L-T-P (Lecture-Tutorial-Practical) structure of 3-0-0.
Is BCAI052 the same subject as KCAI052?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCAI052 and KCAI052 depending on the scheme year. The syllabus content is the same.
Where can I download BCAI052 previous year question papers?
AKTU B.Tech previous year question papers for every semester are available to download free on Ryzenstudy, with no login or registration required.
Is this the official AKTU syllabus for BCAI052?
Yes. Every topic on this page is taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus document (AKTU B.Tech 3rd Year Computer Science and Engineering (Data Science) Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-19.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year Computer Science and Engineering (Data Science) Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-19.