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Department of Computer Science and Technology

 
Principal lecturer: 
Prof Ted Briscoe
Prof Paula Buttery
Students: 
MPhil ACS, Part III
Course code: 
L95
Prerequisites: 
L90: Overview of Natural Language Processing or an equivalent undergraduate course
Hours: 
16
Class limit: 
16

Aims

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.

Syllabus

  • Linguistics for NLP - morphology, syntax, semantics, pragmatics (of English) [6 sessions]
  • Parsing - grammars, treebanks, representations and evaluation, statistical parse ranking [8 sessions]
  • Interpretation - compositional semantics and entailment, pragmatic inference [2 sessions]

Objectives

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 and interpretative inference.

Practical work

  • Week 6: Download and apply a PSG-based parser to a designated text. Evaluate the performance of the tools quantitatively and qualitatively.
  • Week 8: Download and apply two parsers to a designated text. Evaluate the performance of the tools quantitatively and qualitatively.

Assessment

  • There will be four ticked, take-home assignments in the first half of the term. Each assignment is worth 5% of the final mark.
  • An assessed practical report based on the practicals described above. The practical report will consist of a description of the work done of not more than 5000 words. It will contribute 80% 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 COVID-19, the method of teaching for this module will be adjusted to cater for physical distancing and students who are working remotely. We will confirm precisely how the module will be taught closer to the start of term.