J-REED: Joint Relation Extraction and Entity Disambiguation

Dat Ba Nguyen, M. Theobald, G. Weikum
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引用次数: 10

Abstract

Information extraction (IE) from text sources can either be performed as Model-based IE (i.e, by using a pre-specified domain of target entities and relations) or as Open IE (i.e., with no particular assumptions about the target domain). While Model-based IE has limited coverage, Open IE merely yields triples of surface phrases which are usually not disambiguated into a canonical set of entities and relations. This paper presents J-REED: a joint approach for entity disambiguation and relation extraction that is based on probabilistic graphical models. J-REED merges ideas from both Model-based and Open IE by mapping surface names to a background knowledge base, and by making surface relations as crisp as possible.
J-REED:联合关系提取与实体消歧
从文本源中提取信息(IE)既可以作为基于模型的IE(即,通过使用预先指定的目标实体和关系领域)执行,也可以作为Open IE(即,对目标领域没有特定的假设)执行。虽然基于模型的IE覆盖范围有限,但Open IE只产生表面短语的三元组,这些短语通常不会被消歧为规范的实体和关系集。本文提出了一种基于概率图模型的实体消歧和关系抽取的联合方法J-REED。J-REED通过将表面名称映射到背景知识库,并通过使表面关系尽可能清晰,从而合并了来自基于模型和Open IE的想法。
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