Toponym Identification in Epidemiology Articles -- A Deep Learning Approach
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When analyzing the spread of viruses, epidemiologists often need to identify the location of infected hosts. This information can be found in public databases, such as GenBank genebank, however, information provided in these databases are usually limited to the country or state level. More fine-grained localization information requires phylogeographers to manually read relevant scientific articles. In this work we propose an approach to automate the process of place name identification from medical (epidemiology) articles. and resolving ambiguities related to mention of geographical locations in text. detection of toponyms from medical texts. The focus of this paper is to propose a deep learning based model for toponym detection and experiment with the use of external linguistic features and domain specific information. The model was evaluated using a collection of 105 epidemiology articles from PubMed Central Weissenbacher2015 provided by the recent SemEval task 12 semeval-2019-web. Our best detection model achieves an F1 score of 80.13%, a significant improvement compared to the state of the art of 69.84%. These results underline the importance of domain specific embedding as well as specific linguistic features in toponym detection in medical journals.
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