Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.
Objective: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem on instance-label correlations for predicting biome...
| Published in: | Journal of the American Medical Informatics Association Vol. 28; no. 12; pp. 2571 - 2582 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Oxford University Press / USA
Dec2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153871865&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153871865 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Dec2021 vid: 28 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 153871865 153871865 NLM34524450 153871865 10.1093/jamia/ocab176 NLM34524450 153871865 ppf: 2571 ppct: 11 formats: tig: atl: Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning. aug: au: Zhu, Tiantian Qin, Yang Xiang, Yang Hu, Baotian Chen, Qingcai Peng, Weihua affil: Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen) , Shenzhen, China sug: subj: Unified Medical Language System Semantics Comparative Studies Multicenter Studies Evaluation Research Validation Studies Funding Source Human ab: Objective: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem on instance-label correlations for predicting biomedical relations using deep learning and reinforcement learning.Materials and Methods: In this study, a new computational model called piecewise attentive convolutional neural network and reinforcement learning (PACNN+RL) was proposed to perform RE on distantly supervised data generated from Unified Medical Language System with MEDLINE abstracts and benchmark datasets. In PACNN+RL, PACNN was introduced to encode semantic information of biomedical text, and the RL method with memory backtracking mechanism was leveraged to alleviate the erroneous data issue. Extensive experiments were conducted on 4 biomedical RE tasks.Results: The proposed PACNN+RL model achieved competitive performance on 8 biomedical corpora, outperforming most baseline systems. Specifically, PACNN+RL outperformed all baseline methods with the F1-score of 0.5592 on the may-prevent dataset, 0.6666 on the may-treat dataset, and 0.3838 on the DDI corpus, 2011. For the protein-protein interaction RE task, we obtained new state-of-the-art performance on 4 out of 5 benchmark datasets.Conclusions: The performance on many distantly supervised biomedical RE tasks was substantially improved, primarily owing to the denoising effect of the proposed model. It is anticipated that PACNN+RL will become a useful tool for large-scale RE and other downstream tasks to facilitate biomedical knowledge acquisition. We also made the demonstration program and source code publicly available at http://112.74.48.115:9000/. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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