School of Computer Science

Module 06-37810 (2022)

Natural Language Processing

Level 3/H

Semester 2 20 credits
Co-ordinator: Mark Lee
Reviewer: Alan Sexton
The Module Description is a strict subset of this Syllabus Page.

Outline

Natural Language Processing enables computers to understand and reason about human languages such as English and has resulted in many exciting technologies such as conversational assistants, machine translation and (intelligent) internet search. This module would provide the theoretical foundations of NLP as well as applied techniques for extracting and reasoning about information from text.

The module explores three major themes:

  • Computational Models of human cognition such as memory, attention and psycholinguistics

  • Symbolic AI methods for processing language such as automated reasoning, planning, parsing of grammar, and conversational systems.

  • Statistical Models of Language including the use of machine learning to infer structure and meaning.

Learning Outcomes

On successful completion of this module, the student should be able to:

  • Demonstrate an understanding of the major topics in Natural Language Processing

  • Understand the role of machine learning techniques in widening the coverage of NLP systems

  • Demonstrate an ability to apply knowledge-based and statistical techniques to real-world NLP problems

Assessment

  • Main Assessments: Examination (80%) and continuous assessment (20%)
  • Supplementary Assessments: Examination (100%)

Programmes containing this module