Computing

Advanced Natural Language Processing

Module code: 968G5
Level 7 (Masters)
15 credits in spring teaching
Teaching method: Seminar, Laboratory
Assessment modes: Coursework

Advanced Natural Language Processing builds on the foundations provided by the Applied Natural Language Processing module.

You will develop your knowledge and understanding of key topics including:

  • word sense disambiguation
  • vector space models of semantics
  • named entity recognition
  • topic modelling
  • machine translation

Seminars will provide in-depth discussion of research papers related to the key topics and also general issues that arise when developing natural language processing tools, including:

  • hypothesis testing
  • data smoothing techniques
  • domain adaptation
  • generative versus discriminative learning
  • semi-supervised learning

Labs will provide the opportunity for students to improve their python programming skills, experiment with some off-the-shelf technology and develop research skills.

Module learning outcomes

  • Demonstrate a systematic knowledge and understanding of key challenges in the field of natural language processing (NLP) and critical awareness of current approaches to tackling these challenges.
  • Critically analyse state-of-the-art NLP technologies and critically assess their application to novel problems involving large quantities of realistic data.
  • Critically evaluate the effectiveness of an approach through the design and application of suitable experiments.
  • Synthesise and critically assess state-of-the-art technologies for a given NLP problem based on primary scientific literature.