Natural Language Engineering
Module code: G5119
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:
- 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.