Tree kernel-based semantic relation extraction with rich syntactic and semantic information

Tree kernel-based semantic relation extraction with rich syntactic and semantic information
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基于树核的语义关系提取,具有丰富的句法和语义信息

DOI:
10.1016/j.ins.2009.12.006
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发表时间:
2010-04
期刊:
Information Sciences[SCI影响因子3.291]
影响因子:
--
通讯作者:
Zhou Guodong
Zhou Guodong
中科院分区:
其他
文献类型:
--
作者:
Qian Longhua;Fan Jianxi;Zhou Guodong

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本文提出了一种新的基于树核的方法,具有丰富的语法和语义信息的命名实体之间的语义关系的提取。首先,我们利用解析树和实体对,构造一个丰富的语义关系树结构,以整合句法和语义信息。然后提出了一种上下文敏感的卷积树核,它通过将其祖先节点的路径作为上下文来枚举上下文无关和上下文敏感的子树,以捕获树结构中的结构信息。对自动内容抽取/关系检测和特征化(ACE RDC)语料库的评价表明,所提出的基于树核的方法优于其他国家的最先进的方法。
This paper proposes a novel tree kernel-based method with rich syntactic and semantic information for the extraction of semantic relations between named entities. With a parse tree and an entity pair, we first construct a rich semantic relation tree structure to integrate both syntactic and semantic information. And then we propose a context-sensitive convolution tree kernel, which enumerates both context-free and context-sensitive sub-trees by considering the paths of their ancestor nodes as their contexts to capture structural information in the tree structure. An evaluation on the Automatic Content Extraction/Relation Detection and Characterization (ACE RDC) corpora shows that the proposed tree kernel-based method outperforms other state-of-the-art methods.
语义关系抽取中的分层学习策略
DOI: 10.1016/j.ipm.2007.07.007
发表时间: 2008-05
影响因子: 8.6
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