ArticleBioinformatics (Oxford, England)2018
Deep neural networks and distant supervision for geographic location mention extraction.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 29 citations in OpenAlex.
- A comparison of few-shot and traditional named entity recognition models for medical text.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Sequence Matching between Hemagglutinin and Neuraminidase through Sequence Analysis Using Machine Learning.Viruses · 2022Article
- Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation.JMIR public health and surveillance · 2021Article
- GeoBoost2: a natural languageprocessing pipeline for GenBank metadata enrichment for virus phylogeography.Bioinformatics (Oxford, England) · 2020Article
- Going back to the roots: Evaluating Bayesian phylogeographic models with discrete trait uncertainty.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2020Article
- CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision.Bioinformatics (Oxford, England) · 2020Article
- Bi-directional Recurrent Neural Network Models for Geographic Location Extraction in Biomedical Literature.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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 state-of-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.