Deep neural networks and distant supervision for geographic location mention extraction

• Type of Material: Conference Paper
• Authors: Arjun Magge, Arizona State University; Davy Weissenbacher, University of Pennsylvania; Md. Abeed Sarker, Emory University; Matthew Scotch, Arizona State University; Graciela Gonzalez-Hernandez, University of Pennsylvania
• Language: English
• Date: 2018-07-01
• Publisher: Emory University Libraries
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2018. Published by Oxford University Press. All rights reserved.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1093/bioinformatics/bty273
• Title of Journal or Parent Work: Bioinformatics
• Conference or Event Name: 26th Annual Conference on Intelligent Systems for Molecular Biology (ISMB)
• Volume: 34
• Issue: 13
• Start Page: 565
• End Page: 573
• Grant/Funding Information: Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases (NIAID) of the National Institutes of Health (NIH) under grant number R01AI117011.
• Keywords: Life Sciences & Biomedicine; Statistics & Probability; Mathematical & Computational Biology; Science & Technology; Computer Science, Interdisciplinary Applications; Physical Sciences; Biochemistry & Molecular Biology; Mathematics; Biochemical Research Methods; Biotechnology & Applied Microbiology; Technology; Computer Science
• Research Categories: Engineering, Biomedical; Biology, Biostatistics

Abstract

Motivation: Virus phylogeographers rely on DNA sequences of viruses and the locations of the infected hosts found in public sequence databases like GenBank for modeling virus spread. However, the locations in GenBank records are often only at the country or state level, and may require phylogeographers to scan the journal articles associated with the records to identify more localized geographic areas. To automate this process, we present a named entity recognizer (NER) for detecting locations in biomedical literature. We built the NER using a deep feedforward neural network to determine whether a given token is a toponym or not. To overcome the limited human annotated data available for training, we use distant supervision techniques to generate additional samples to train our NER. Results: Our NER achieves an F1-score of 0.910 and significantly outperforms the previous stateof- the-art system. Using the additional data generated through distant supervision further boosts the performance of the NER achieving an F1-score of 0.927. The NER presented in this research improves over previous systems significantly. Our experiments also demonstrate the NER?s capability to embed external features to further boost the system?s performance. We believe that the same methodology can be applied for recognizing similar biomedical entities in scientific literature.