Applied Natural Language Processing (955G5)
15 credits, Level 7 (Masters)
Autumn teaching
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:
- tokenisation
- segmentation
- stemming
- lemmatisation
- 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.
Teaching
50%: Lecture
50%: Practical (Laboratory)
Assessment
30%: Coursework (Report)
70%: Examination (Computer-based examination)
Contact hours and workload
This module is approximately 150 hours of work. This breaks down into about 44 hours of contact time and about 106 hours of independent study. The Â鶹´«Ã½ may make minor variations to the contact hours for operational reasons, including timetabling requirements.
We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum.
We’ll make sure to let you know of any material changes to modules at the earliest opportunity.