Details
| Original language | English |
|---|---|
| Title of host publication | HT 2011 - Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 271-280 |
| Number of pages | 10 |
| ISBN (print) | 9781450302562 |
| Publication status | Published - 6 Jun 2011 |
Publication series
| Name | HT 2011 - Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia |
|---|
Abstract
Event-Based Epidemic Intelligence (e-EI) has arisen as a body of work which relies upon different forms of pattern recognition in order to detect the disease reporting events from unstructured text that is present on the Web. Current supervised approaches to e-EI suffer both from high initial and high maintenance costs, due to the need to manually label examples to train and update a classifier for detecting disease reporting events in dynamic information sources, such as blogs. In this paper, we propose a new method for the supervised detection of disease reporting events. We tackle the burden of manually labeling data and address the problems associated with building a supervised learner to classify frequently evolving, and variable blog content. We automatically clas- sify outbreak reports to train a supervised learner, and the knowledge acquired from the learning process is then trans- ferred to the task of classifying blogs. Our experiments show that with the automatic classification of training data, and the transfer approach, we achieve an overall precision of 92% and an accuracy of 78.20%.
Keywords
- Automatic labeling, Epidemic intelligence, Transfer learning
ASJC Scopus subject areas
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
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HT 2011 - Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia. Association for Computing Machinery (ACM), 2011. p. 271-280 (HT 2011 - Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Transfer Approach to Detecting Disease Reporting Events in Blog Social Media
AU - Stewart, Avaré
AU - Smith, Matthew
AU - Nejdl, Wolfgang
PY - 2011/6/6
Y1 - 2011/6/6
N2 - Event-Based Epidemic Intelligence (e-EI) has arisen as a body of work which relies upon different forms of pattern recognition in order to detect the disease reporting events from unstructured text that is present on the Web. Current supervised approaches to e-EI suffer both from high initial and high maintenance costs, due to the need to manually label examples to train and update a classifier for detecting disease reporting events in dynamic information sources, such as blogs. In this paper, we propose a new method for the supervised detection of disease reporting events. We tackle the burden of manually labeling data and address the problems associated with building a supervised learner to classify frequently evolving, and variable blog content. We automatically clas- sify outbreak reports to train a supervised learner, and the knowledge acquired from the learning process is then trans- ferred to the task of classifying blogs. Our experiments show that with the automatic classification of training data, and the transfer approach, we achieve an overall precision of 92% and an accuracy of 78.20%.
AB - Event-Based Epidemic Intelligence (e-EI) has arisen as a body of work which relies upon different forms of pattern recognition in order to detect the disease reporting events from unstructured text that is present on the Web. Current supervised approaches to e-EI suffer both from high initial and high maintenance costs, due to the need to manually label examples to train and update a classifier for detecting disease reporting events in dynamic information sources, such as blogs. In this paper, we propose a new method for the supervised detection of disease reporting events. We tackle the burden of manually labeling data and address the problems associated with building a supervised learner to classify frequently evolving, and variable blog content. We automatically clas- sify outbreak reports to train a supervised learner, and the knowledge acquired from the learning process is then trans- ferred to the task of classifying blogs. Our experiments show that with the automatic classification of training data, and the transfer approach, we achieve an overall precision of 92% and an accuracy of 78.20%.
KW - Automatic labeling
KW - Epidemic intelligence
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=79960181601&partnerID=8YFLogxK
U2 - 10.1145/1995966.1996001
DO - 10.1145/1995966.1996001
M3 - Conference contribution
AN - SCOPUS:79960181601
SN - 9781450302562
T3 - HT 2011 - Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia
SP - 271
EP - 280
BT - HT 2011 - Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia
PB - Association for Computing Machinery (ACM)
ER -