Grounded Feature Selection for Biomedical Relation Extraction by the Combinative Approach

S. Song, G. Heo, Ha Jin Kim, H. Jung, Yonghwan Kim, Min Song
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引用次数: 10

Abstract

Relation extraction is an important task in biomedical areas such as protein-protein interaction, gene-disease interactions, and drug-disease interactions. In recent years, it has been widely researched to automatically extract biomedical relations in a vest amount of biomedical text data. In this paper, we propose a hybrid approach to extracting relations based on a rule-based approach feature set. We then use different classification algorithms such as SVM, Naïve Bayes, and Decision Tree classifiers for relation classification. The rationale for adopting shallow parsing and other NLP techniques to extract relations is two-folds: simplicity and robustness. We select seven features with the rule-based shallow parsing technique and evaluate the performance with four different PPI public corpora. Our experimental results show the stable performance in F-measure even with the relatively fewer features.
基于组合方法的生物医学关系提取接地特征选择
关系提取是蛋白质-蛋白质相互作用、基因-疾病相互作用、药物-疾病相互作用等生物医学领域的重要研究课题。近年来,从大量的生物医学文本数据中自动提取生物医学关系已经得到了广泛的研究。在本文中,我们提出了一种基于基于规则的方法特征集的混合方法来提取关系。然后,我们使用不同的分类算法,如SVM、Naïve贝叶斯和决策树分类器进行关系分类。采用浅层解析和其他NLP技术提取关系的基本原理有两个方面:简单性和健壮性。我们使用基于规则的浅解析技术选择了7个特征,并使用4个不同的PPI公共语料库评估了性能。我们的实验结果表明,即使特征相对较少,F-measure的性能也很稳定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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