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Course overview: The 10-day 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 nltk (a Python toolkit).
Requirements: Python knowledge is required.
Learning outcomes:
Subject specific: Knowledge, Understanding and Skills
On successful completion of this module 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.
Learning Outcomes: 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