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A Novel Composite Kernel Approach to Chinese Entity Relation Extraction

机译:中文实体关系抽取的一种新型复合核方法

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摘要

Relation extraction is the task of finding semantic relations between two entities from the text. In this paper, we propose a novel composite kernel for Chinese relation extraction. The composite kernel is defined as the combination of two independent kernels. One is the entity kernel built upon the non-content-related features. The other is the string semantic similarity kernel concerning the content information. Three combinations, namely linear combination, semi-polynomial combination and polynomial combination are investigated. When evaluated on the ACE 2005 Chinese data set, the results show that the proposed approach is effective.
机译:关系提取是从文本中查找两个实体之间的语义关系的任务。在本文中,我们提出了一种用于中文关系提取的新型复合核。复合内核定义为两个独立内核的组合。一种是基于非内容相关功能的实体内核。另一个是涉及内容信息的字符串语义相似性内核。研究了线性组合,半多项式组合和多项式组合这三种组合。当在ACE 2005中文数据集上进行评估时,结果表明该方法是有效的。

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