University of Information Technology

Computational Linguistics

Course Description

The intent of the course is to introduction to Natural Language Processing (NLP, a.k.a. computational linguistics), the study of computing systems that can process, understand, or communicate in human language. Natural Language Processing (NLP) is a rapidly developing field with broad applicability throughout the hard sciences, social sciences, and the humanities. The ability to harness, employ and analyze linguistic and textual data effectively is a highly desirable skill for academic work, in government, and throughout the private sector. This course is intended as a theoretical and methodological introduction to a the most widely used and effective current techniques, strategies and toolkits for natural language processing, with a primary focus on those available in the Python programming language.

The objectives of the course are

  • To understand natural language processing and to learn how to apply basic algorithms in this field.
  • To gain an in-depth understanding of the computational properties of natural languages and the commonly used algorithms for processing linguistic information.
  • To get acquainted with the algorithmic description of the main language levels: morphology, syntax, semantics, and pragmatics, as well as the resources of natural language data – corpora.
  • To provide students with the knowledge of basic characteristics of speech signal in relation to production and hearing of speech by humans.

Intended Learning Outcomes

Upon the successful completion of this course, students should be able to:

  • Identity the NLP tasks’ challenges in Myanmar Language.
  • Explore the principles of language resource annotation and its use in machine learning applications and apply the above principles in analysis of data and acquire intended information through the use of available tools.
  • Apply the fundamental mathematical models and Natural Language Processing techniques in applications in software design and implementation for NLP applications.
  • Implement NLP subproblems (tokenizing, tagging, parsing) with NLP tools.
  • Apply the concepts and techniques for NLP applications (sentiment analysis, text classification, information retrieval, information extraction).
  • Experiment with both theoretical and practical aspects in NLP applications.

Text and References Books


  1. Speech and Language processing, Daniel Jurafsky and James H.Martin, 2017
  2. The Handbook of Computational Linguistics and Natural Language Processing, Alexander Clark, Chris Fox and Shalom Lappin, 2010
  3. Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper, 2010
  4. Foundations of Statistical Natural Language Processing, Christopher D. Manning Hinrich, The MIT Press, 2000

Assessment system

Evaluation Marks Percentage
Class Participation 10 Marks 10%
Tutorial 10 Marks 10%
Assignments/Discussion/Presentation 20 Marks 20%
Final Examination 60 Marks 60%