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Taking a Dive: Experiments in Deep Learning for Automatic Ontology-based Annotation of Scientific Literature

View ORCID ProfilePrashanti Manda, Lucas Beasley, View ORCID ProfileSomya D. Mohanty
doi: https://doi.org/10.1101/365874
Prashanti Manda
1Department of Computer Science, University of North Carolina at Greensboro, NC, USA
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Lucas Beasley
1Department of Computer Science, University of North Carolina at Greensboro, NC, USA
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Somya D. Mohanty
1Department of Computer Science, University of North Carolina at Greensboro, NC, USA
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I. Abstract

Text mining approaches for automated ontology-based curation of biological and biomedical literature have largely focused on syntactic and lexical analysis along with machine learning. Recent advances in deep learning have shown increased accuracy for textual data annotation. However, the application of deep learning for ontology-based curation is a relatively new area and prior work has focused on a limited set of models.

Here, we introduce a new deep learning model/architecture based on combining multiple Gated Recurrent Units (GRU) with a character+word based input. We use data from five ontologies in the CRAFT corpus as a Gold Standard to evaluate our model’s performance. We also compare our model to seven models from prior work. We use four metrics - Precision, Recall, F1 score, and a semantic similarity metric (Jaccard similarity) to compare our model’s output to the Gold Standard. Our model resulted in a 84% Precision, 84% Recall, 83% F1, and a 84% Jaccard similarity. Results show that our GRU-based model outperforms prior models across all five ontologies. We also observed that character+word inputs result in a higher performance across models as compared to word only inputs.

These findings indicate that deep learning algorithms are a promising avenue to be explored for automated ontology-based curation of data. This study also serves as a formal comparison and guideline for building and selecting deep learning models and architectures for ontology-based curation.

Footnotes

  • p_manda{at}uncg.edu, sdmohant{at}uncg.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 10, 2018.
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Taking a Dive: Experiments in Deep Learning for Automatic Ontology-based Annotation of Scientific Literature
Prashanti Manda, Lucas Beasley, Somya D. Mohanty
bioRxiv 365874; doi: https://doi.org/10.1101/365874
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Taking a Dive: Experiments in Deep Learning for Automatic Ontology-based Annotation of Scientific Literature
Prashanti Manda, Lucas Beasley, Somya D. Mohanty
bioRxiv 365874; doi: https://doi.org/10.1101/365874

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