Master in Computational Linguistics Syllabus 2026: Subjects, Specializations, and Semester-wise Syllabus

Students learn programming languages like Python, and study algorithms for text analysis, sentiment analysis, and machine translation.

Syllabus: Master's in Computational Linguistics

A Master's in Computational Linguistics syllabus typically covers core topics in linguistics, natural language processing, and computer science. It includes courses in syntax, semantics, phonology, and morphology, as well as machine learning, data mining, and statistical modeling. Students learn programming languages like Python, and study algorithms for text analysis, sentiment analysis, and machine translation. Advanced coursework may delve into deep learning, neural networks, and speech recognition. Practical applications, research projects, and internships are often part of the curriculum, allowing students to gain hands-on experience in developing linguistic software and working with large-scale language datasets.

1st Year OR 1st & 2nd Semester Syllabus of Master in Computational Linguistics

S.no	Subjects
1	Comparative Historical Linguistics
2	Phonetics
3	Introductory Transformational Generative Syntax
4	Aspects of Linguistic Behavior
5	Computational Linguistics Toolkit
6	Applied Linguistics

2nd Year OR 3rd & 4th Semester Syllabus of Master in Computational Linguistics

S.No	Subjects
1	Language and Mind
2	Semantics
3	Historical Linguistics
4	Computer Applications for Sanskrit
5	Lexicography
6	Multimedia Storage and Retrieval System for Culture