蛋白质标注的学习关系和信息提取规则

Jee Hyub Kim, M. Hilario
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引用次数: 0

摘要

蛋白质注释是根据主题Y描述蛋白质X的任务,到目前为止,大多数蛋白质注释工作都是由人类注释者手动完成的。然而,随着生物医学论文数量的快速增长,人工标注变得越来越困难,对蛋白质标注过程自动化的需求越来越大。近年来,信息抽取(Information Extraction, IE)被用于解决这一问题。通常,IE需要预定义的关系和手工制作的IE规则或带注释的语料库,而这些要求在现实世界的领域(如生物医学领域)很难满足。在本文中,我们描述了一个IE系统,该系统只需要由领域专家标记的与给定主题相关或不相关的句子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning Relations and Information Extraction Rules for Protein Annotation
Protein annotation is a task that describes protein X in terms of topic Y Until now, most of protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever rapidly, manual annotation becomes difficult, and there is increasing need to automate the protein annotation process. Recently, Information Extraction (IE) has been used to solve this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real world domains such as the biomedical domain. In this paper, we describe an IE system which requires only sentences labeled relevant or not to a given topic by domain experts.
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