生物医学文献中的蛋白质关系抽取研究

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摘要蛋白质关系抽取研究对于生命科学各领域的研究具有广泛的应用价值。但是,基于机器学习的蛋白质关系抽取方法普遍停留在二元关系抽取,失去了丰富的关系类型信息,而基于规则的开放式信息抽取方法可以抽取完整的蛋白质关系(“蛋白质1,关系词,蛋白质2”),但是召回率较低。针对以上问题,该文提出了一种混合机器学习和规则方法的蛋白质关系抽取框架。该框架先利用机器学习方法完成命名实体识别和二元关系抽取,然后利用基于句法模板和词典匹配的方法抽取表示当前两个蛋白质间关系类型的关系词。该方法在AImed语料上取得了40.18%的F值,远高于基于规则的Stanford Open IE方法。

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	赵哲焕
	杨志豪
	孙聪
	林鸿飞

关键词 ：关系词抽取, 蛋白质实体识别, 蛋白质关系抽取

Abstract：Protein-protein interaction extraction can be widely applied to the field of life science research. Most of the machine learning based methods focused on binary relationship extraction for high precision, while the rule based strategy can extract complex relations (“protein1, relational word, protein2”) with low recall. This paper proposes a hybrid protein-protein interaction extraction method. In this method, machine learning methods are first applied to recognize protein entities and extract relational protein pairs. Then, the syntactic patterns and a dictionary are employed to find out corresponding relational words that represent the relationship between two proteins. This method obtains a F-score of 40.18% on the AImed corpus, outperforming any of the two methods alone.

Key words： relational words extraction protein named entity recognition protein-protein interaction extraction

收稿日期: 2017-08-31

中图分类号:

TP391

基金资助:国家重点研发计划项目(2016YFC0901902);国家自然科学基金(61572102,61572098,61272373);教育部跨世纪优秀人才培养计划(NCET-13-0084);引进人才科研专题(DUT18RC(3)004)

作者简介: 赵哲焕(1986—),博士,讲师,主要研究领域为面向生物领域文本挖掘、自然语言处理、深度学习、机器学习。E-mail:z.zhao@dlut.edu.cn
杨志豪(1973—),通信作者,博士,教授,主要研究领域为面向生物领域文本挖掘、自然语言处理、深度学习、机器学习。E-mail:yangzh@dlut.edu.cn
孙聪(1987—),博士研究生,主要研究领域为面向生物领域文本挖掘、自然语言处理、深度学习、机器学习。E-mail:suncong132@mail.dlut.edu.cn

链接本文:

http://jcip.cipsc.org.cn/CN/Y2018/V32/I7/82

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