Applied Natural Language Processing
Module code: 955G5
Level 7 (Masters)
15 credits in autumn semester
Teaching method: Laboratory, Lecture, Class
Assessment modes: Coursework, Unseen examination
Applied Natural Language Processing concerns the theory and practice of automatic text processing technologies.
In this module, 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.
You gain hands-on experience with the practical aspects of this module through weekly laboratory sessions.
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
- Given a novel scenario in which automatic text analysis could potentially be of value, assess whether there is scope for successful deployment of NLP technology.
- Design and implement a system involving generic NLP tools that is suited to a particular problem, selecting approaches that are well-suited to the specific scenario under consideration.
- Formulate a clear verifiable hypothesis that forms the basis of an attempt to successfully deploy NLP technology.
- Use appropriate experimental methods to reliably determine the effectiveness of an NLP software tool on actual data.