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Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations
BACKGROUND: The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology. Some previous investigations conducted the study by applyi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6889521/ https://www.ncbi.nlm.nih.gov/pubmed/31795930 http://dx.doi.org/10.1186/s12859-019-3217-3 |
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author | Jettakul, Amarin Wichadakul, Duangdao Vateekul, Peerapon |
author_facet | Jettakul, Amarin Wichadakul, Duangdao Vateekul, Peerapon |
author_sort | Jettakul, Amarin |
collection | PubMed |
description | BACKGROUND: The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology. Some previous investigations conducted the study by applying feature-based models; others have presented deep-learning-based models such as convolutional and recurrent neural networks used with the shortest dependency paths (SDPs). Although SDPs contain valuable and concise information, some parts of crucial information that is required to define bacterial location relationships are often neglected. Moreover, the traditional word-embedding used in previous studies may suffer from word ambiguation across linguistic contexts. RESULTS: Here, we present a deep learning model for biomedical RE. The model incorporates feature combinations of SDPs and full sentences with various attention mechanisms. We also used pre-trained contextual representations based on domain-specific vocabularies. To assess the model’s robustness, we introduced a mean F1 score on many models using different random seeds. The experiments were conducted on the standard BB corpus in BioNLP-ST’16. Our experimental results revealed that the model performed better (in terms of both maximum and average F1 scores; 60.77% and 57.63%, respectively) compared with other existing models. CONCLUSIONS: We demonstrated that our proposed contributions to this task can be used to extract rich lexical, syntactic, and semantic features that effectively boost the model’s performance. Moreover, we analyzed the trade-off between precision and recall to choose the proper cut-off to use in real-world applications. |
format | Online Article Text |
id | pubmed-6889521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68895212019-12-11 Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations Jettakul, Amarin Wichadakul, Duangdao Vateekul, Peerapon BMC Bioinformatics Research Article BACKGROUND: The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology. Some previous investigations conducted the study by applying feature-based models; others have presented deep-learning-based models such as convolutional and recurrent neural networks used with the shortest dependency paths (SDPs). Although SDPs contain valuable and concise information, some parts of crucial information that is required to define bacterial location relationships are often neglected. Moreover, the traditional word-embedding used in previous studies may suffer from word ambiguation across linguistic contexts. RESULTS: Here, we present a deep learning model for biomedical RE. The model incorporates feature combinations of SDPs and full sentences with various attention mechanisms. We also used pre-trained contextual representations based on domain-specific vocabularies. To assess the model’s robustness, we introduced a mean F1 score on many models using different random seeds. The experiments were conducted on the standard BB corpus in BioNLP-ST’16. Our experimental results revealed that the model performed better (in terms of both maximum and average F1 scores; 60.77% and 57.63%, respectively) compared with other existing models. CONCLUSIONS: We demonstrated that our proposed contributions to this task can be used to extract rich lexical, syntactic, and semantic features that effectively boost the model’s performance. Moreover, we analyzed the trade-off between precision and recall to choose the proper cut-off to use in real-world applications. BioMed Central 2019-12-03 /pmc/articles/PMC6889521/ /pubmed/31795930 http://dx.doi.org/10.1186/s12859-019-3217-3 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Jettakul, Amarin Wichadakul, Duangdao Vateekul, Peerapon Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations |
title | Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations |
title_full | Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations |
title_fullStr | Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations |
title_full_unstemmed | Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations |
title_short | Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations |
title_sort | relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6889521/ https://www.ncbi.nlm.nih.gov/pubmed/31795930 http://dx.doi.org/10.1186/s12859-019-3217-3 |
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