Natural Language Processing (NLP)
Date: 17th June – 21st June 2019
Venue: Computer Room A & B, Main Building, Institute of Psychiatry, Psychology & Neurosciences (IoPPN), King's College London. View Map.
The course provides an introduction to the nature of medical text, and the technical and organisational challenges encountered when processing.
The course will be based around practical examples and widely used NLP tools.
The course aims to provide an introduction to the major techniques of natural language processing, and to provide participants with the skills to create their own NLP applications, using both rules based and statistical approaches. Methods will be introduced for extracting structured information from text, and for automatically classifying text, together with the selection of data for training and for evaluation.
The course will discuss aspects of NLP that are specific to medical and biomedical texts, and develop a critical awareness of the issues associated with these texts. Participants will be equipped with the skills needed to analyse their own language processing problems, to design and test solutions, and to understand the limitations of those solutions.
The course will provide a practical instruction in the use of some widely used tools in NLP, including GATE (a Java based framework) and Python.
Subject specific: Knowledge, Understanding and Skills
On successful completion of this course the student should be able to:
- Understand the complexities of language/data
- Explain when and why NLP is appropriate.
- Interpret NLP paradigms in new and complex contexts to assess their suitability.
- Apply or implement a NLP paradigm to a specific problem.
- Demonstrate deeper analytical skills by
- Showing an understanding of different evaluation techniques
- Using their knowledge to evaluate their practice in terms of the NLP model or method used.
General: Knowledge, Understanding and Skills
On successful completion of this module the student should be able to
- Identify the key features of a corpus of textual data
- Elicit the client (or their own) requirements for the analysis of a corpus of textual
- data, and recommend solutions for the analysis of that data
- Show confidence in the use of NLP software to conduct analysis of textual data
- Interact effectively in a research team (group activities)
- Interpret the results of their experimental work
- Communicate the results of their work effectively
Cost and Booking
- External Early bird: £855 (till 25/04/19, price thereafter £950)
- KCL Staff Early bird: £641.25 (till 25/04/19, price thereafter £712.5)
- KCL Student Early bird: £427.5 (till 25/04/19, price thereafter £475)
- Other student Early bird: £641.25 (till 25/04/19, price thereafter £712.5)
- King's Health Partners Early bird: £641.25 (till 25/04/19, price thereafter £712.5)
That is, 50% discount to King's College London PhD students, 25% discount to other students and staff at King's College London and King's Health Partners.
Booking for this course has now closed.
Dr Angus Roberts (Academic Lead)
Dr Sumithra Velupillai
Angus is a senior lecturer in health informatics at the Department of Biostatistics and Health Informatics, King's College London.
See Angus' research portal here.
I am a lecturer in Applied Health Informatics at the Institute of Psychiatry, Psychology and Neuroscience (IoPPN) at King’s College London, UK and NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust.
My main research interests are in the area of Clinical Natural Language Processing (NLP), in particular semantic and contextual analysis. Over the last few years I have worked on creating annotated resources and developing NLP tools for e.g. uncertainty detection and temporal expressions in Swedish and English clinical texts.
See Sumithra's research portal here.
Your place will not be confirmed until payment has been made. Failure to cancel without sufficient notice will forfeit your course fee and access to future courses. If you would like to pay by internal transfer, please contact email@example.com