tailieunhanh - Báo cáo khoa học: "A Composite Kernel to Extract Relations between Entities with both Flat and Structured Features"

This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. . | A Composite Kernel to Extract Relations between Entities with both Flat and Structured Features Min Zhang Jie Zhang Jian Su Guodong Zhou Institute for Infocomm Research 21 Heng Mui Keng Terrace Singapore 119613 mzhang zhangjie sujian zhougd @ Abstract This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering and can also easily scale to include more features. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly outperforms previous two dependency tree kernels for relation extraction. 1 Introduction The goal of relation extraction is to find various predefined semantic relations between pairs of entities in text. The research on relation extraction has been promoted by the Message Understanding Conferences MUCs MUC 19871998 and Automatic Content Extraction ACE program ACE 2002-2005 . According to the ACE Program an entity is an object or set of objects in the world and a relation is an explicitly or implicitly stated relationship among entities. For example the sentence Bill Gates is chairman and chief software architect of Microsoft Corporation. conveys the ACE-style relation between the entities Bill Gates and Microsoft Corporation ORGANIZATION. Commercial . In this paper we address the problem of relation extraction using kernel methods Scholkopf and Smola 2001 . Many feature-based learning algorithms involve only the dot-product between feature .