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Department of Computer Science and Technology



Course pages 2022–23

Introduction to Natural Language Syntax and Parsing

Principal lecturer: Prof Paula Buttery
Taken by: MPhil ACS, Part III
Code: L95
Term: Michaelmas
Hours: 16
Class limit: max. 16 students
Prerequisites: Natural Language Processing. L90: Overview of Natural Language Processing or an equivalent undergraduate course
Moodle, timetable


This module aims to provide a brief introduction to linguistics for computer scientists and then goes on to cover some of the core tasks in natural language processing (NLP), focussing on statistical parsing of sentences to yield syntactic and semantic representations. We will look at how to evaluate parsers and see how well state-of-the-art tools perform given current techniques.


  • Linguistics for NLP - morphology, syntax, semantics
  • Parsing - grammars, treebanks, representations and evaluation, statistical parse 
  • Interpretation - compositional semantics 


On completion of this module, students should:

  • understand the basic properties of human languages and be familiar with descriptive and theoretical frameworks for handling these properties;
  • understand the design of tools for NLP tasks such as parsing and be able to apply them to text and evaluate their performance;
  • understand some of the basic principles of the representation of linguistic meaning.

Practical work

  • Week 1-7: There will be non-assessed practical exercises between sessions and during sessions.
  • Week 8: Download and apply two parsers to a designated text. Evaluate the performance of the tools quantitatively and qualitatively.


  • There will be one presentation 10% of the final mark; presentation topics will be allocated at the start of term.
  • An assessed practical report of not more than 5000 words. It will contribute 90% of the final mark.

Recommended reading

Jurafsky, D. and Martin, J. (2008). Speech and Language Processing. Prentice-Hall (2nd ed.). (See also 3rd ed. available online.)

Further Information

Due to infectious respiratory diseases, the method of teaching for this module may be adjusted to cater for physical distancing and students who are working remotely. Unless otherwise advised, this module will be taught in person.