Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | String Processing and Information Retrieval - 18th International Symposium, SPIRE 2011, Proceedings |
| Seiten | 87-103 |
| Seitenumfang | 17 |
| Publikationsstatus | Veröffentlicht - 2011 |
| Veranstaltung | 18th International Symposium on String Processing and Information Retrieval, SPIRE 2011 - Pisa, Italien Dauer: 17 Okt. 2011 → 21 Okt. 2011 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 7024 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Content analysis and clustering of natural language documents becomes crucial in various domains, even in public health. Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. Information should be gathered from a broader range of sources, including the Web which in turn requires more robust processing capabilities. To address this limitation, in this paper, we propose a new approach to detect public health events in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 62% and a recall of 75% evaluated using manually annotated, real-world data.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
Ziele für nachhaltige Entwicklung
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String Processing and Information Retrieval - 18th International Symposium, SPIRE 2011, Proceedings. 2011. S. 87-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 7024 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Detecting health events on the social web to enable epidemic intelligence
AU - Fisichella, Marco
AU - Stewart, Avaré
AU - Cuzzocrea, Alfredo
AU - Denecke, Kerstin
PY - 2011
Y1 - 2011
N2 - Content analysis and clustering of natural language documents becomes crucial in various domains, even in public health. Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. Information should be gathered from a broader range of sources, including the Web which in turn requires more robust processing capabilities. To address this limitation, in this paper, we propose a new approach to detect public health events in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 62% and a recall of 75% evaluated using manually annotated, real-world data.
AB - Content analysis and clustering of natural language documents becomes crucial in various domains, even in public health. Recent pandemics such as Swine Flu have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. Information should be gathered from a broader range of sources, including the Web which in turn requires more robust processing capabilities. To address this limitation, in this paper, we propose a new approach to detect public health events in an unsupervised manner. We address the problems associated with adapting an unsupervised learner to the medical domain and in doing so, propose an approach which combines aspects from different feature-based event detection methods. We evaluate our approach with a real world dataset with respect to the quality of article clusters. Our results show that we are able to achieve a precision of 62% and a recall of 75% evaluated using manually annotated, real-world data.
KW - Clustering
KW - Epidemic Intelligence
KW - Retrospective medical event detection
UR - http://www.scopus.com/inward/record.url?scp=80053963768&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24583-1_10
DO - 10.1007/978-3-642-24583-1_10
M3 - Conference contribution
AN - SCOPUS:80053963768
SN - 9783642245824
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 87
EP - 103
BT - String Processing and Information Retrieval - 18th International Symposium, SPIRE 2011, Proceedings
T2 - 18th International Symposium on String Processing and Information Retrieval, SPIRE 2011
Y2 - 17 October 2011 through 21 October 2011
ER -