Journal of the Korean Society for Library and Information Science
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- P-ISSN1225-598X
- E-ISSN2982-6292
An Experimental Study on the Relation Extraction from Biomedical Abstracts using Machine Learning
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Abstract
This paper introduces a relation extraction system that can be used in identifying and classifying semantic relations between biomedical entities in scientific texts using machine learning methods such as Support Vector Machines (SVM). The suggested system includes many useful functions capable of extracting various linguistic features from sentences having a pair of biomedical entities and applying them into training relation extraction models for maximizing their performance. Three globally representative collections in biomedical domains were used in the experiments which demonstrate its superiority in various biomedical domains. As a result, it is most likely that the intensive experimental study conducted in this paper will provide meaningful foundations for research on bio-text analysis based on machine learning.
- keywords
- 관계 추출, 지지벡터기계, 단백질 간 상호작용 추출, 텍스트 마이닝, 기계 학습, Relation Extraction, Support Vector Machines, Protein-Protein Interaction Extraction, Text Mining, Machine Learning
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