Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2011
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/66456 |
| _version_ | 1826207166932975616 |
|---|---|
| author | Ryan, Russell J. (Russell John Wyatt) |
| author2 | Özlem Uzuner and Peter Szolovits. |
| author_facet | Özlem Uzuner and Peter Szolovits. Ryan, Russell J. (Russell John Wyatt) |
| author_sort | Ryan, Russell J. (Russell John Wyatt) |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. |
| first_indexed | 2024-09-23T13:45:05Z |
| format | Thesis |
| id | mit-1721.1/66456 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:45:05Z |
| publishDate | 2011 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/664562019-04-10T18:00:39Z Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language Ground truth budgeting Novel approach to semi-supervised relation extraction in medical language Ryan, Russell J. (Russell John Wyatt) Özlem Uzuner and Peter Szolovits. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 67-69). We address the problem of weakly-supervised relation extraction in hospital discharge summaries. Sentences with pre-identified concept types (for example: medication, test, problem, symptom) are labeled with the relationship between the concepts. We present a novel technique for weakly-supervised bootstrapping of a classifier for this task: Groundtruth Budgeting. In the case of highly-overlapping, self-similar datasets as is the case with the 2010 i2b2/VA challenge corpus, the performance of classifiers on the minority classes is often poor. To address this we set aside a random portion of the groundtruth at the beginning of bootstrapping which will be gradually added as the classifier is bootstrapped. The classifier chooses groundtruth samples to be added by measuring the confidence of its predictions on them and choosing samples for which it has the least confident predictions. By adding samples in this fashion, the classifier is able to increase its coverage of the decision space while not adding too many majority-class examples. We evaluate this approach on the 2010 i2b2/VA challenge corpus containing of 477 patient discharge summaries and show that with a training corpus of 349 discharge summaries, budgeting 10% of the corpus achieves equivalent results to a bootstrapping classifier starting with the entire corpus. We compare our results to those of other papers published in the proceedings of the 2010 Fourth i2b2/VA Shared-Task and Workshop. by Russell J. Ryan. M.Eng. 2011-10-17T21:28:01Z 2011-10-17T21:28:01Z 2011 2011 Thesis http://hdl.handle.net/1721.1/66456 756040752 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 69 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Ryan, Russell J. (Russell John Wyatt) Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language |
| title | Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language |
| title_full | Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language |
| title_fullStr | Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language |
| title_full_unstemmed | Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language |
| title_short | Groundtruth budgeting : a novel approach to semi-supervised relation extraction in medical language |
| title_sort | groundtruth budgeting a novel approach to semi supervised relation extraction in medical language |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/66456 |
| work_keys_str_mv | AT ryanrusselljrusselljohnwyatt groundtruthbudgetinganovelapproachtosemisupervisedrelationextractioninmedicallanguage AT ryanrusselljrusselljohnwyatt groundtruthbudgeting AT ryanrusselljrusselljohnwyatt novelapproachtosemisupervisedrelationextractioninmedicallanguage |