EXPLOITING THE LANGUAGE OF MODERATED SOURCES FOR CROSS-CLASSIFICATION OF USER GENERATED CONTENT

Avaré Stewart, Wolfgang Nejdl

Abstract

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. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. Existing systems are limited in that they rely on template-driven approaches to extract information about public health events from human language text. In this paper, we propose a new approach to support Epidemic Intelligence. We tackle the problem of detecting relevant information from unstructured text from a statistical pattern recognition viewpoint. In doing so, we also address the problems associated with the noisy and dynamic nature of blogs by exploiting the language in moderated sources, to train a classifier for detecting victim reporting sentences in blog social media. We refer to this as Cross-Classification. Our experiments show that without using manually labeled data, and with a simple set of features, we are able to achieve a precision as high as 88% and an accuracy of 77%, comparable with the state-of-the-art approaches for the same task.

References

  1. Conway, M., Collier, N., and Doan, S. (2009). Using hedges to enhance a disease outbreak report text mining system. In BioNLP 7809: Proceedings of the Workshop on BioNLP, pages 142-143, Morristown, NJ, USA. Association for Computational Linguistics.
  2. Fuxman, A., Kannan, A., Goldberg, A. B., Agrawal, R., Tsaparas, P., and Shafer, J. (2009). Improving classification accuracy using automatically extracted training data. In KDD 7809: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1145-1154, New York, NY, USA. ACM.
  3. Hartley, D., Nelson, N., Walters, R., Arthur, R., Yangarber, R., Madoff, L., Linge, J., Mawudeku, A., Collier, N., Brownstein, J., Thinus, G., and Lightfoot, N. (2009). The landscape of international event-based biosurveillance. Emerging Health Threats.
  4. Lam-Adesina, A. M. and Jones, G. J. F. (2001). Applying summarization techniques for term selection in relevance feedback. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 7801, pages 1-9, New York, NY, USA. ACM.
  5. Moens, M.-F. (2009). Information extraction from blogs. In Jansen, B. J., Spink, A., and Taksa, I., editors, Handbook of Research on Web Log Analysis, pages 469- 487. IGI Global.
  6. Moschitti, A. (2006). Making tree kernels practical for natural language learning. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics.
  7. Pan, S. J. and Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 99.
  8. Tomasic, A., Simmons, I., and Zimmerman, J. (2007). Learning information intent via observation. In WWW 7807: Proceedings of the 16th international conference on World Wide Web, pages 51-60, New York, NY, USA. ACM.
  9. Zhang, Y. (2008). Automatic Extraction of Outbreak Information from News. PhD thesis, University of Illinois at Chicago.
Download


Paper Citation


in Harvard Style

Stewart A. and Nejdl W. (2011). EXPLOITING THE LANGUAGE OF MODERATED SOURCES FOR CROSS-CLASSIFICATION OF USER GENERATED CONTENT . In Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8425-51-5, pages 571-576. DOI: 10.5220/0003298105710576


in Bibtex Style

@conference{webist11,
author={Avaré Stewart and Wolfgang Nejdl},
title={EXPLOITING THE LANGUAGE OF MODERATED SOURCES FOR CROSS-CLASSIFICATION OF USER GENERATED CONTENT},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2011},
pages={571-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003298105710576},
isbn={978-989-8425-51-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - EXPLOITING THE LANGUAGE OF MODERATED SOURCES FOR CROSS-CLASSIFICATION OF USER GENERATED CONTENT
SN - 978-989-8425-51-5
AU - Stewart A.
AU - Nejdl W.
PY - 2011
SP - 571
EP - 576
DO - 10.5220/0003298105710576