Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature
Joint Authors
Yang, Zhihao
Feng, Qinlin
Gui, Yingyi
Wang, Lei
Li, Yuxia
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-10-16
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
With the rapid growth of biomedical literature, a large amount of knowledge about diseases, symptoms, and therapeutic substances hidden in the literature can be used for drug discovery and disease therapy.
In this paper, we present a method of constructing two models for extracting the relations between the disease and symptom and symptom and therapeutic substance from biomedical texts, respectively.
The former judges whether a disease causes a certain physiological phenomenon while the latter determines whether a substance relieves or eliminates a certain physiological phenomenon.
These two kinds of relations can be further utilized to extract the relations between disease and therapeutic substance.
In our method, first two training sets for extracting the relations between the disease-symptom and symptom-therapeutic substance are manually annotated and then two semisupervised learning algorithms, that is, Co-Training and Tri-Training, are applied to utilize the unlabeled data to boost the relation extraction performance.
Experimental results show that exploiting the unlabeled data with both Co-Training and Tri-Training algorithms can enhance the performance effectively.
American Psychological Association (APA)
Feng, Qinlin& Gui, Yingyi& Yang, Zhihao& Wang, Lei& Li, Yuxia. 2016. Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature. BioMed Research International،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1097375
Modern Language Association (MLA)
Feng, Qinlin…[et al.]. Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature. BioMed Research International No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1097375
American Medical Association (AMA)
Feng, Qinlin& Gui, Yingyi& Yang, Zhihao& Wang, Lei& Li, Yuxia. Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1097375
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1097375