Under Graduate Programmes:

BCA (Bachelor of Computer Applications)

The BCA programme imparts comprehensive knowledge of computer applications with equal emphasis on theory and practice. A BCA graduate would be able to demonstrate advanced skills in the effective analysis, design and realization of business systems utilizing contemporary information technology.

Upon completion of the BCA programme, students will be able to:

  • Use and apply current technical concepts and practices in the core computer applications.
  • Identify computer application related problems, analyze them and design the system or provide the solution for the problem considering legal, ethical and societal issues.
  • Recognize the need for and an ability to engage in continuing professional development.
  • Work and communicate effectively in interdisciplinary environment, either independently or in team, and demonstrate scientific leadership in academia and industry.
  • Communicate effectively by oral, written, computing and graphical means.

Career Scope:

After completing BCA course, students have opportunities to work as database administrators, computer programmers, system administrators or computer support service specialists in reputed IT companies. BCA graduates can also pursue Master level degree or Post Graduate Diploma courses to widen their career prospects.


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Sem - I

Sem - II

Sem - III

Sem - IV

Sem - V

Sem - VI

NATURAL LANGUAGE PROCESSING

UNIT-I

                                                                                                           [9 SESSIONS]

Introduction to Natural Language Processing:  

Introduction to text pre-processing, terminologies related with text processing, challenges of text pre-processing, tokenization, sentence segmentation, introduction to lexical analysis, finite state morphonology, finite state morphology, morphology vs lexical analysis, paradigm based lexical analysis.

UNIT-II

                                                                                                           [9 SESSIONS]

Syntactic Parsing and Semantic Analysis:  

Introduction to syntactic parsing, The Cocke–Kasami–Younger Algorithm, parsing as deduction, Implementing Deductive Parsing, LR Parsing, Constraint-based Grammars, Issues in Parsing, Basic Concepts and Issues in Natural Language Semantics, Theories and Approaches to Semantic Representation, Relational Issues in Lexical Semantics, Fine-Grained Lexical-Semantic Analysis.

UNIT-III

                                                                                                           [9 SESSIONS]

Context- Natural Language Generation: 

Introduction to natural language generation, simple Examples of Generated Texts, The Components of a Generator: Components and level of representation, Approaches to Text Planning: The Linguistic Component: Surface Realization Components, Relationship to Linguistic Theory, Chunk Size, Assembling vs. Navigating, Systemic Grammars, Functional Unification Grammars.

UNIT-IV

                                                                                                           [9 SESSIONS]

Corpus Creation: 

Introduction and definition of corpus in natural language processing, corpus size, Balance, Representativeness, and Sampling, Data Capture and Copyright, Corpus Markup and Annotation, Multilingual Corpora, Multimodal Corpora, Corpus Annotation Types, Morphosyntactic Annotation, Treebanks: Syntactic, Semantic, and Discourse Annotation, The Process of Building Treebanks, application of Treebanks.

UNIT-V

                                                                                                                   [9 SESSIONS]

Statistical Techniques in Natural Language Processing 

Introduction to statistics and its importance in natural language processing, general linear model, binary linear classification, one versus all method for multi-category classification, maximum likelihood estimation in parameter estimation in linear classification techniques, concepts of generative and discriminative models, introduction to sequence prediction model and its application in natural language processing