@Article{cmc.2020.07711,
AUTHOR = {Xiaolei Ma, Yang Lu, Yinan Lu, Zhili Pei, Jichao Liu},
TITLE = {Biomedical Event Extraction Using a New Error Detection  Learning Approach Based on Neural Network},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {2},
PAGES = {923--941},
URL = {http://www.techscience.com/cmc/v63n2/38552},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.07711}
}