Open Access
ARTICLE
Biomedical Event Extraction Using a New Error Detection Learning Approach Based on Neural Network
Xiaolei Ma1, 2, Yang Lu1, 2, Yinan Lu1, *, Zhili Pei2, Jichao Liu3
1 College of the Computer Science and Technology, Jilin University, Changchun, 130012, China.
2 College of the Computer Science and Technology, Inner Mongolia University for Nationalities, Tongliao, 028000, China.
3 School of Science and Technology, Yanching Institute of Technology, Langfang, 065202, China.
* Corresponding Author: Yinan Lu. Email: .
Computers, Materials & Continua 2020, 63(2), 923-941. https://doi.org/10.32604/cmc.2020.07711
Received 21 June 2019; Accepted 04 November 2019; Issue published 01 May 2020
Abstract
Supervised machine learning approaches are effective in text mining, but their
success relies heavily on manually annotated corpora. However, there are limited numbers
of annotated biomedical event corpora, and the available datasets contain insufficient
examples for training classifiers; the common cure is to seek large amounts of training
samples from unlabeled data, but such data sets often contain many mislabeled samples,
which will degrade the performance of classifiers. Therefore, this study proposes a novel
error data detection approach suitable for reducing noise in unlabeled biomedical event
data. First, we construct the mislabeled dataset through error data analysis with the
development dataset. The sample pairs’ vector representations are then obtained by the
means of sequence patterns and the joint model of convolutional neural network and long
short-term memory recurrent neural network. Following this, the sample identification
strategy is proposed, using error detection based on pair representation for unlabeled data.
With the latter, the selected samples are added to enrich the training dataset and improve
the classification performance. In the BioNLP Shared Task GENIA, the experiments
results indicate that the proposed approach is competent in extract the biomedical event
from biomedical literature. Our approach can effectively filter some noisy examples and
build a satisfactory prediction model.
Keywords
Cite This Article
X. Ma, Y. Lu, Y. Lu, Z. Pei and J. Liu, "Biomedical event extraction using a new error detection learning approach based on neural network,"
Computers, Materials & Continua, vol. 63, no.2, pp. 923–941, 2020. https://doi.org/10.32604/cmc.2020.07711