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Improving Clinical Named-Entity Recognition with Transfer Learning
Edmond Zhang1, Quentin Thurier1, Luke Boyle1
1Orion Health, New Zealand.
Studies in Health Technology and Informatics
|July 25, 2018
Summary
Transfer learning significantly improves natural language processing (NLP) models by reusing prior knowledge. This technique enhances named-entity recognition (NER) performance, even with poorly annotated data.
Area of Science:
- Machine Learning
- Natural Language Processing (NLP)
Background:
- Transfer learning is widely used in computer vision but less explored in NLP.
- Leveraging prior knowledge is key for developing advanced AI models.
Purpose of the Study:
- To investigate the application of transfer learning in NLP.
- To enhance NLP model accuracy for specific tasks like named-entity recognition (NER).
Main Methods:
- Applied transfer learning to build NLP models.
- Evaluated model performance on a named-entity recognition (NER) task.
- Utilized knowledge from a base model trained on imperfectly labeled data.
Main Results:
- Demonstrated significantly improved recognition performance in NER.
- Showcased the effectiveness of transfer learning in NLP contexts.
- Validated the benefit of leveraging existing knowledge from base models.
Conclusions:
- Transfer learning is a viable and powerful technique for advancing NLP.
- Prior knowledge transfer enhances model accuracy, especially in data-scarce or noisy scenarios.
- This approach offers a pathway to more robust and accurate NLP applications.
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