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Sortal anaphora resolution to enhance relation extraction from biomedical literature

机译:分类回指解析以增强从生物医学文献中提取关系的能力

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摘要

BackgroundEntity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization. Coreference resolution is a foundational yet challenging natural language processing task which, if performed successfully, is likely to enhance such systems significantly. In this paper, we propose a semantically oriented, rule-based method to resolve sortal anaphora, a specific type of coreference that forms the majority of coreference instances in biomedical literature. The method addresses all entity types and relies on linguistic components of SemRep, a broad-coverage biomedical relation extraction system. It has been incorporated into SemRep, extending its core semantic interpretation capability from sentence level to discourse level.
机译:背景实体共指在生物医学文献中很常见,它会影响依赖于对命名实体的准确标识(例如关系提取和自动摘要)的文本理解系统。共指解析是一项基本但具有挑战性的自然语言处理任务,如果成功执行,很可能会大大增强此类系统。在本文中,我们提出了一种基于语义的,基于规则的方法来解决分类指照,这是生物医学文献中形成大多数共指实例的共指的一种特殊类型。该方法可处理所有实体类型,并依赖于SemRep的语言组件,SemRep是一种覆盖面广的生物医学关系提取系统。它已被合并到SemRep中,将其核心语义解释功能从句子级别扩展到了话语级别。

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