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Biomedical Event Extraction Using a New Error Detection Learning Approach Based on Neural Network

Computers, Materials & Continua 2020年第5期63卷 923-941页

作者： Xiaolei Ma Yang Lu Yinan Lu Zhili Pei Jichao Liu College of the Computer Science and Technology Jilin UniversityChangchun130012China College of the Computer Science and Technology Inner Mongolia University for NationalitiesTongliao028000China School of Science and Technology Yanching Institute of TechnologyLangfang065202China

Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated ***,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 ***,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event ***,we construct the mislabeled dataset through error data analysis with the development *** 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 *** this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled *** the latter,the selected samples are added to enrich the training dataset and improve the classification *** the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical *** approach can effectively filter some noisy examples and build a satisfactory prediction model.

关键词： Biomedical event extraction pair representation error data detection sample identification

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