School of Computer Science

Module 06-02495 (2014)

Natural Language Processing 1

Level 2/I

John Barnden Semester 2 10 credits
Co-ordinator: John Barnden
Reviewer: Peter Hancox

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

Outline

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. Examples are mostly given using Prolog.


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:

  1. describe major trends and systems in Natural Language Processing
  2. define: morphology; syntax; semantics; pragmatics; and give appropriate examples to illustrate their definitions
  3. 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
  4. describe simple feature-based semantic systems
  5. demonstrate a knowledge of at least one method for resolving pronoun referents as an example of semantic interpretation
  6. describe an application of natural language processing (for instance machine translation) and show the place of syntactic, semantic and pragmatic processing

Restrictions

None


Teaching methods

2 hrs/week lectures and exercise classes.

Contact Hours: 23


Assessment

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".


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