Module 02495 (2015)

Syllabus page 2015/2016

Natural Language Processing 1

Level 2/I

John Barnden
10 credits in Semester 2

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus

The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

Relevant Links

Lecturer's web pages for the module


The module presents an overview of Natural Language Processing and its applications, followed by introductions to morphology, syntax and semantics. These topics are used to introduce some linguistic theory and appropriate algorithms for their computational implementation.


The aims of this module are to:

  • introduce Natural Language Processing as one of the components of Artificial Intelligence, both from engineering and cognitive viewpoints
  • provide foundations for the programming of Natural Language Processing techniques.

Learning Outcomes

On successful completion of this module, the student should be able to: Assessed by:
1describe major trends and systems in Natural Language Processing Examination
2define: morphology; syntax; semantics; pragmatics; and give appropriate examples to illustrate their definitions Examination, Continuous Assessment
3describe several standard methods of applying morphological and syntactic knowledge in Natural Language Processing systems, for instance: finite-state methods; probabilistic methods; context-free grammars and parsers, including the Active Chart Parsers; unification grammars and parsing Examination, Continuous Assessment
4describe simple feature-based semantic systems Examination, Continuous Assessment
5demonstrate a knowledge of at least one method for resolving pronoun referents as an example of semantic interpretation Examination
6describe an application of natural language processing (for instance machine translation) and show the place of syntactic, semantic and pragmatic processing Examination

Restrictions, Prerequisites and Corequisites








Teaching Methods:

2 hrs/week lectures and exercise classes.

Contact Hours:



  • Sessional: 1.5 hr examination (80%), continuous assessment (20%).
  • Supplementary (where allowed): By examination only.
  • The nature and timing of the continuous assessment will be specified on the module web page -- see under "Relevant Links".

Recommended Books

TitleAuthor(s)Publisher, Date
Speech and language processing: an introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (2nd ed) Jurafsky D & Martin J HPrentice Hall, 2008

Detailed Syllabus

  1. Introduction and Overview (1 week)
  2. Finite State Networks & Transducers (1 week)
  3. Simple grammars (1 week)
  4. Basic parsing algorithms (1 week)
  5. Active chart parsing (1 week)
  6. Features (1 week)
  7. Semantics (1 week)
  8. Discourse Representation Theory (1 week)
  9. Discourse (1 week)
    • Cohesion in Text
    • Pronoun Reference Resolution
  10. Pragmatics (1 week)
    • Speech Acts
    • Pragmatic Inference
  11. Applications (1 week)

Last updated: 19 Aug 2015

Source file: /internal/modules/COMSCI/2015/xml/02495.xml

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus