Bi-directional recurrent neural network models for geographic location extraction in biomedical literature

Arjun Magge, Davy Weissenbacher, Abeed Sarker, Matthew Scotch, Graciela Gonzalez-Hernandez

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)100-111
Number of pages12
JournalPacific Symposium on Biocomputing
Volume24
Issue number2019
StatePublished - 2019
Event24th Pacific Symposium on Biocomputing, PSB 2019 - Kohala Coast, United States
Duration: Jan 3 2019Jan 7 2019

Keywords

  • Deep Learning
  • Named Entity Recognition
  • Natural Language Processing
  • Toponym Detection
  • Toponym Disambiguation
  • Toponym Resolution

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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