Papers by Ken Barker
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification (D19-1)
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| Challenge: | supervised learning models perform poorly at low-shot tasks for which little labeled data is available for training. |
| Approach: | They propose to combine a bag-of-words embedding approach and a context-aware method to improve low-shot text classification. |
| Outcome: | The proposed method improves low-shot text classification with pre-training and rationales . the simple bag-of-words approach is the clear top performer when there are few training instances or less . |
Leveraging Medical Literature for Section Prediction in Electronic Health Records (D19-1)
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| Challenge: | Prior approaches to section prediction have only used text data from EHRs and required significant manual annotation. |
| Approach: | They propose to use sections from medical literature to train models to predict sections in EHRs. |
| Outcome: | The proposed model uses sections from medical literature that contain similar content to those found in EHR sections. |