Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model for retrospective news event detection
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning information intent via observation
Proceedings of the 16th international conference on World Wide Web
A Comparative Study of Methods for Transductive Transfer Learning
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Improving classification accuracy using automatically extracted training data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic extraction of outbreak information from news
Automatic extraction of outbreak information from news
Active dual supervision: reducing the cost of annotating examples and features
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Using hedges to enhance a disease outbreak report text mining system
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Can one language bootstrap the other: a case study on event extraction
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Extending Semi-supervised Learning Methods for Inductive Transfer Learning
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Knowledge Discovery in the Blogosphere: Approaches and Challenges
IEEE Internet Computing
Sentence-level event classification in unstructured texts
Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Applicability of recommender systems to medical surveillance systems
Proceedings of the second international workshop on Web science and information exchange in the medical web
Making use of social media data in public health
Proceedings of the 21st international conference companion on World Wide Web
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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 labelling data and address the problems associated with building a supervised learner to classify frequently evolving, and variable blog content. We automatically classify outbreak reports to train a supervised learner, and the knowledge acquired from the learning process is then transferred 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%.