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1、基于上下文环境和句法分析的蛋白质关系抽取摘要:针对蛋白质交互作用关系(ppi)抽取方法中特征利用的片面性问题,提出了一种从上下文环境和句法结构中抽取特征的方法。该方法抽取词法特征、位置特征、距离特征、依存句法特征和深层句法特征等丰富特征构成特征集,并且使用支持向量机(svm)分类器进行ppi抽取。方法在5个公开的ppi语料上进行了评估。实验结果表明,丰富特征有效地利用了更为全面的信息,避免丢失重要特征的危险,得到了较好的ppi抽取性能。即在aimed语料上的实验取得了59.2%的f值和85.6%的曲线下面积(auc)值。关键词:
2、信息抽取;自然语言处理;蛋白质关系抽取;特征;支持向量机protein.proteininteractionextractionbasedoncontextualandsyntacticfeatureswangjian*,jiming.hui,linhong.fei,yangzhi.haoschoolofcomputerscienceandtechnology,dalianuniversityoftechnology,dalianliaoning116024,chinaabstract:consideringone-sid
3、ednessoffeaturesusedinmanyprotein-proteininteraction(ppi)extractionmethods.anovelapproachisproposedtoextractrichfeaturesfromcontextinformationandsyntaxstructureforppiextraction.variousfeatures,suchaslexical,position,distance,dependencysyntaxanddeepsyntaxfeaturesareextr
4、acts,andthesupportvectormachine(svm)classifierisusedforppiextraction.experimentalevaluationonmultipleppicorporarevealsthattherichfeaturescanutilizemorecomprehensiveinformationtoreducethedangerofmissingsomeimportantfeatures.thismethodachievesstate-of-the-artperformancew
5、ithrespecttocomparableevaluations,with59.2%f-scoreand85.6%aucontheaimedcorpus.consideringtheone.sidednessoffeaturesusedinmanyprotein.proteininteraction(ppi)extractionmethods,anewapproachwasproposedtoextractrichfeaturesfromcontextinformationandsyntaxstructureforppiextra
6、ction.variousfeatures,suchaslexicon,position,distance,dependencysyntaxanddeepsyntaxfeaturesconstitutefeatureset,andthesupportvectormachine(svm)classifierwasusedforppiextraction.theexperimentalevaluationonmultipleppicorporarevealsthattherichfeaturescanutilizemorecompreh
7、ensiveinformationtoreducetheriskofmissingsomeimportantfeatures.thismethodachievesstate.of.the.artperformancewithrespecttocomparableevaluations,with59.2%f.scoreand85.6%areaundercurve(auc)ontheaimedcorpus.keywords:informationextraction;naturallanguageprocessing;protein.
8、proteininteraction(ppi)extraction;feature;supportvectormachine(svm)0引言生物医学文献中的蛋白质交互作用关系(protein.proteininteraction,