This module is offered in 2016-17.

Aims

The aims of this module are:

  • To explain various techniques used to automatically process language, pertaining to perception and understanding as well as to generation of language.

Learning Outcomes

On successful completion of this module, the student should:

  • Have an understanding of modern approaches to automatic natural language processing (NLP).
  • Have an understanding of statistical and symbolic approaches to model human language processing.
  • Be able to build (parts of) small NLP applications.

Syllabus

  • Components of language, such as morphology, syntax and semantics.
  • Computational models of components of language, such as N-grams, formal grammars and logical expressions.
  • General techniques, such as machine learning, dynamic programming and search.
  • Applications of NLP.

Compulsory Elements

This module has no compulsory elements beyond those common to all modules (mark of 4 in each assessment component).

Module Delivery

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Last Published: 01 Mar 2017.