Protein-protein interaction extraction based on contextual and syntactic features

doi:10.3724/SP.J.1087.2012.01074

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (04): 1074-1077.DOI: 10.3724/SP.J.1087.2012.01074

• Database technology • Previous Articles Next Articles

Protein-protein interaction extraction based on contextual and syntactic features

WANG Jian,JI Ming-hui,LIN Hong-fei,YANG Zhi-hao

School of Computer Science and Technology, Dalian University of Technology, Dalian Liaoning 116024, China

Received:2011-09-26 Revised:2011-11-21 Online:2012-04-20 Published:2012-04-01
Contact: WANG Jian

基于上下文环境和句法分析的蛋白质关系抽取

王健,冀明辉,林鸿飞,杨志豪

大连理工大学计算机科学与技术学院，辽宁大连 116024

通讯作者: 王健
作者简介:王健（1967-），女，黑龙江双城人，副教授，博士研究生，CCF高级会员，主要研究方向：信息检索、文本挖掘；
冀明辉（1988-），女，内蒙古乌兰察布人，硕士研究生，主要研究方向：蛋白质关系抽取；
林鸿飞（1962-），男，内蒙古通辽人，教授，博士生导师，博士，主要研究方向：搜索引擎、文本挖掘、情感计算、自然语言处理；
杨志豪（1973-），男，黑龙江大庆人，副教授，博士，主要研究方向：信息检索、文本挖掘、自然语言处理。
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;国家863高科技计划资助项目;教育部留学回国人员科研启动基金和高等学校博士学科点专项科研基金

Abstract

Abstract: Considering the one-sidedness of features used in many Protein-Protein Interaction (PPI) extraction methods, a new approach was proposed to extract rich features from context information and syntax structure for PPI extraction. Various features, such as lexicon, position, distance, dependency syntax and deep syntax features constitute feature set, and the Support Vector Machine (SVM) classifier was used for PPI extraction. The experimental evaluation on multiple PPI corpora reveals that the rich features can utilize more comprehensive information to reduce the risk of missing some important features. This method achieves state-of-the-art performance with respect to comparable evaluations, with 59.2% F-score and 85.6% Area Under Curve (AUC) on the AImed corpus.

Key words: information extraction, natural language processing, Protein-Protein Interaction (PPI) extraction, feature, Support Vector Machine (SVM)

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: 信息抽取, 自然语言处理, 蛋白质关系抽取, 特征, 支持向量机

CLC Number:

TP391.1

WANG Jian JI Ming-hui LIN Hong-fei YANG Zhi-hao. Protein-protein interaction extraction based on contextual and syntactic features[J]. Journal of Computer Applications, 2012, 32(04): 1074-1077.

王健冀明辉林鸿飞杨志豪. 基于上下文环境和句法分析的蛋白质关系抽取[J]. 计算机应用, 2012, 32(04): 1074-1077.

References

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Protein-protein interaction extraction based on contextual and syntactic features

基于上下文环境和句法分析的蛋白质关系抽取

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