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中文摘要：
蛋白质关系抽取是生物医学信息抽取领域的重要分支。目前研究中,基于特征和核函数方法的蛋白质关系抽取已被充分研究,并且达到了很高的F-值,通过改进特征和核函数进一步优化实例表示变得十分困难。该文结合词表示和深层神经网络,提出了一种实例表示模型。该模型能够充分利用词表示的语义表示能力和深层神经网络的表示优化能力;同时引入主成分分析和特征选择进行特征优化,并且通过比较多种传统的分类器,寻找适合蛋白质关系抽取的分类器。该方法在AIMed语料、BioInfer语料和HPRD50语料上的F-值分别取得了70.5%、82.2%和80.0%,在蛋白质关系抽取任务上达到了目前最好的抽取水平。
英文摘要：
Protein-Protein Interaction extraction （PPIE） is a significant topic in biomedical text mining. Most of the current researches on PPI are based on kernels and features. To further boost the performance, this paper presents an improved instance representation model integrating word representation and deep neural network. Meanwhile, the model incorporates feature selection, PCA and different kinds of classifiers, and finds the best combinations for PPI extraction. Experimental results show that the method is significantly better than other state-of-art methods on three public PPI corpora： AIMed, Biolnfer, HPRD50, achieving the F-scores of 70.5%, 82.2% and 80.0%, respectively.
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