  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, 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. 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 provided by the recent SemEval task
12. 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.
