School of Computer Science

Module 06-02495 (2012)

Natural Language Processing 1

Level 2/I

Mark Lee Semester 2 10 credits
Co-ordinator: Mark Lee
Reviewer: Peter Hancox

The Module Description is a strict subset of this Syllabus Page.

Aims

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
  • show how Natural Language Processing techniques can be programmed using the Prolog programming language

Learning Outcomes

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

  • describe major trends and systems in Natural Language Processing
  • define: morphology; syntax; semantics; pragmatics; and give appropriate examples to illustrate their definitions
  • describe 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
  • implement context-free grammars for simple English
  • describe simple feature-based semantic systems typically based on logic showing the difference between building semantic representations and interpreting semantic representations
  • demonstrate a knowledge of at least one method for resolving pronoun referents as an example of semantic interpretation
  • show an understanding of the role of pragmatics in understanding natural language
  • describe an application of natural language processing (for instance machine translation) and show the place of syntactic, semantic and pragmatic processing

Teaching methods

2 hrs/week lectures and exercise classes.


Assessment

  • Sessional: 1.5 hr examination (80%), continuous assessment (20%).
  • Supplementary: By examination only.

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)

Programmes containing this module