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中文摘要：
这份报纸建议在命名实体之间的语义关系察觉和分类(RDC ) 的一个树核方法。它在 RDC 的以前的树核方法解决二个批评问题。首先，一个新树内核被介绍更好由与上下文易感知并且子树的近似匹配启用标准卷绕旋转树内核在一棵分析树上捕获固有的结构的信息。第二，充实的分析树结构被建议很好导出必要结构的信息，例如，合适的潜伏的注解，从一棵分析树。ACE RDC 语料库上的评估证明新树核和充实的分析树结构显著地作出贡献到 RDC 和我们大部分超过最先进的树核方法。
英文摘要：
This paper proposes a tree kernel method of semantic relation detection and classification （RDC） between named entities. It resolves two critical problems in previous tree kernel methods of RDC. First, a new tree kernel is presented to better capture the inherent structural information in a parse tree by enabling the standard convolution tree kernel with context-sensitiveness and approximate matching of sub-trees. Second, an enriched parse tree structure is proposed to well derive necessary structural information, e.g., proper latent annotations, from a parse tree. Evaluation on the ACE RDC corpora shows that both the new tree kernel and the enriched parse tree structure contribute significantly to RDC and our tree kernel method much outperforms the state-of-the-art ones.
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Label propagation via bootstrapped support vectors for semantic relation extraction between named en
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