Abstract
Analyzing how people discuss about health-related topics on dedicated forums and social networks such as Twitter, can provide valuable insight for syndromic surveillance and to predict disease outbreaks. In this paper we present a minimally trained algorithm to learn associations between technical and everyday language terms, based on pattern generalization and complete linkage clustering, and we then assess its utility on a case study of five common syndromes for surveillance purposes.
Original language | English |
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Title of host publication | International Conference Recent Advances in Natural Language Processing, RANLP |
Pages | 640-648 |
Number of pages | 9 |
Publication status | Published - 2013 |
Event | 9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013 - Hissar, Bulgaria Duration: Sep 9 2013 → Sep 11 2013 |
Other
Other | 9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013 |
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Country/Territory | Bulgaria |
City | Hissar |
Period | 9/9/13 → 9/11/13 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Software
- Electrical and Electronic Engineering