Natural Language Engineering

Module code: G5119
Level 5
15 credits in autumn semester
Teaching method: Class
Assessment modes: Coursework

In this module, you are introduced to techniques and concepts involved in the analysing of text by machine - with particular emphases on various practical applications that this technology drives.

You study core, generic text processing models, such as:

  • segmentation
  • stemming
  • part-of-speech tagging
  • named entity recognition
  • phrasal chunking
  • dependency parsing.

You also cover related problems and application areas, such as:

  • document classification
  • information retrieval
  • information extraction.

As part of this, you make extensive use of the Natural Language Toolkit, which is a collection of natural language processing tools written in the Python programming language.

Module learning outcomes

  • Deploy generic NLP technologies to large quantities of realistic data.
  • Design and run an empirical investigation that would establish whether or not there is scope for successfully deploy existing text processing technologies.
  • Determine which language processing technologies would be effective in a given scenario.
  • Build a prototype system that combines off-the-shelf technologies into a practical language processing system.