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l3sintern:research_seminar_10 [2011/01/13 12:15] skoutas |
l3sintern:research_seminar_10 [2011/01/13 12:29] (current) siberski |
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**organized by: ** Dimitris | **organized by: ** Dimitris | ||
- | Speakers: Julien, Marco | + | Speakers: Dimitris, Julien, Marco |
==== Topic(s)==== | ==== Topic(s)==== | ||
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This work is in progress, and I will present the general approach and ideas, as well as | This work is in progress, and I will present the general approach and ideas, as well as | ||
the current status of the work. | the current status of the work. | ||
+ | |||
+ | === Detecting Health Events on the Social Web to Enable Epidemic Intelligence (Marco) === | ||
+ | |||
+ | 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. There is a need to allow | ||
+ | for information gathering 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. | ||
+ | |||
===== Jan 21 ==== | ===== Jan 21 ==== |