Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature

Arjun Magge (1), Davy Weissenbacher (2), Abeed Sarker (2), Matthew Scotch (1) and Graciela Gonzalez-Hernandez (2)

(1) Department of Biomedical Informatics
Biodesign Center for Environmental Health Engineering
Arizona State University
Tempe, AZ, USA

(2) Department of Biostatistics, Epidemiology and Informatics
Perelman School of Medicine
University of Pennsylvania
Philadelphia, PA, USA

Abstract

Phylogeography research involving virus spread and tree reconstruction relies on accurate geographic locations of infected hosts. Insufficient level of geographic information in nucleotide sequence repositories such as GenBank motivates the use of natural language processing methods for extracting geographic location names (toponyms) in the scientific article associated with the sequence, and disambiguating the locations to their co-ordinates. In this paper, we present an extensive study of multiple recurrent neural network architectures for the task of extracting geographic locations and their effective contribution to the disambiguation task using population heuristics. The methods presented in this paper achieve a strict detection F1 score of 0.94, disambiguation accuracy of 91% and an overall resolution F1 score of 0.88 that are significantly higher than previously developed methods, improving our capability to find the location of infected hosts and enrich metadata information.

Keywords: Named Entity Recognition; Toponym Detection; Toponym Disambiguation; Toponym Resolution; Natural Language Processing; Deep Learning;

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