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Tree Kernel-Based Semantic Relation Extraction using Unified Dynamic Relation Tree

机译:基于树内核的语义关系利用统一动态关系树提取

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This paper proposes a Unified Dynamic Relation Tree (DRT) span for tree kernel-based semantic relation extraction between entity names. The basic idea is to apply a variety of linguistics-driven rules to dynamically prune out noisy information from a syntactic parse tree and include necessary contextual information. In addition, different kinds of entity-related semantic information are unified into the syntactic parse tree. Evaluation on the ACE RDC 2004 corpus shows that the Unified DRT span outperforms other widely-used tree spans, and our system achieves comparable performance with the state-of-the-art kernel-based ones. This indicates that our method can not only well model the structured syntactic information but also effectively capture entity-related semantic information.
机译:本文提出了一个统一的动态关系树(DRT)跨度用于实体名称之间的树内核的语义关系提取。基本思想是应用各种语言学驱动的规则,以动态从语法解析树上动态修复嘈杂的信息,并包括必要的上下文信息。此外,不同类型的实体相关语义信息统一到语法解析树中。 ACE RDC 2004语料库的评估表明,统一的DRT跨度优于其他广泛使用的树跨度,我们的系统通过最先进的内核的基于最先进的内核实现了可比性。这表明我们的方法不仅可以根据结构化的句法信息的良好模型,而且还可以有效地捕获与实体相关的语义信息。

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