Ontology Driven Machine learning Approach for Disease Name Extraction from Twitter Messages
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Twitter and social media as a whole has great
potential as a source of disease surveillance data however the
general messiness of tweets presents several challenges for
standard information extraction methods. Current methods
for disease surveillance on twitter rely on inflexible keyword
based approaches that require messages to be pre-filtered on
the basis of a disease name which is supplied a priori and are
not capable of detecting new ailments. In this paper we present
an ontology based machine learning approach to extract
disease names and expressions describing ailments from tweets
which may be employed as part of a larger general purpose
system for automated disease incidence monitoring. We also
propose a simple methodology for automatic detection and
correction of errors.
Description
Keywords
Entity recognition, knowledge engineering, Epidemiology, Ontology
Citation
Mwebaze, E., Nabende, P., & Magumba, M. A. (2017). Ontology Driven Machine learning Approach for Disease Name Extraction from Twitter Messages. 2nd IEEE International Conference on Computational Intelligence and Applications.