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Chemical-gene relation extraction using recursive neural network

Authors
Lim, SangrakKang, Jaewoo
Issue Date
21-6월-2018
Publisher
OXFORD UNIV PRESS
Citation
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Indexed
SCIE
SCOPUS
Journal Title
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/131777
DOI
10.1093/database/bay060
ISSN
1758-0463
Abstract
In this article, we describe our system for the CHEMPROT task of the BioCreative VI challenge. Although considerable research on the named entity recognition of genes and drugs has been conducted, there is limited research on extracting relationships between them. Extracting relations between chemical compounds and genes from the literature is an important element in pharmacological and clinical research. The CHEMPROT task of BioCreative VI aims to promote the development of text mining systems that can be used to automatically extract relationships between chemical compounds and genes. We tested three recursive neural network approaches to improve the performance of relation extraction. In the BioCreative VI challenge, we developed a tree-Long Short-Term Memory networks (tree-LSTM) model with several additional features including a position feature and a subtree containment feature, and we also applied an ensemble method. After the challenge, we applied additional pre-processing steps to the tree-LSTM model, and we tested the performance of another recursive neural network model called Stack-augmented Parser Interpreter Neural Network (SPINN). Our tree-LSTM model achieved an F-score of 58.53% in the BioCreative VI challenge. Our tree-LSTM model with additional pre-processing and the SPINN model obtained F-scores of 63.7 and 64.1%, respectively.
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