An Empirical Study of Sections in Classifying Disease Outbreak Reports
Abstract
Identifying articles that relate to infectious diseases is a necessary step for any automatic bio-surveillance system that monitors news articles from the Internet. Unlike scientific articles which are available in a strongly structured form, news articles are usually loosely structured. In this chapter, we investigate the importance of each section and the effect of section weighting on performance of text classification. The experimental results show that (1) classification models using the headline and leading sentence achieve a high performance in terms of F-score compared to other parts of the article; (2) all section with bag-of-word representation (full text) achieves the highest recall; and (3) section weighting information can help to improve accuracy.
Cite
@article{arxiv.1911.09319,
title = {An Empirical Study of Sections in Classifying Disease Outbreak Reports},
author = {Son Doan and Mike Conway and Nigel Collier},
journal= {arXiv preprint arXiv:1911.09319},
year = {2019}
}
Comments
13 pages, 2 tables, book chapter in Web-Based Applications in Healthcare and Biomedicine. Annals of Information Systems, vol 7. Springer, Boston, MA, 2010