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Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations.

Abstract：

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.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Jettakul A,Wichadakul D,Vateekul P

doi

10.1186/s12859-019-3217-3

subject

Has Abstract

pub_date

2019-12-03 00:00:00

pages

627

issue

1

issn

1471-2105

pii

10.1186/s12859-019-3217-3

journal_volume

20

pub_type

杂志文章

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