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 concepts, trends, approaches/systems, and difficulties in Natural Language Processing and the study of language generally. Examination
2Discuss and illustrate the potential distinctions between morphology, syntax, semantics and pragmatics 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; dependency parsing. Examination, Continuous Assessment
4Describe the basics of symbolic and statistical semantic algorithms. Examination, Continuous Assessment
5Demonstrate knowledge of at least one method for a task such as pronoun reference resolution, coreference resolution, or named-entity recognition as an example of a specific, core task in interpretation. Examination, Continuous Assessment
6Describe an application of natural language processing (for instance machine translation or document summarization) and show the place of syntactic, semantic and pragmatic processing. Examination, Continuous Assessment

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
  2. Nature of language, words, word classes, lexical ambiguity, morphology, etc.
  3. Part-of-speech tagging.
  4. Grammars and parsing.
  5. Semantics: symbolic and statistical approaches.
  6. Pragmatics, cohesion, discourse structure.
  7. Areas of special difficulty such as idioms, figurative language, speech acts, textese.
  8. Applications (may be interspersed through above topics).

Last updated: 15 January 2016

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

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