基于多轮分析的科学OpenIE多词规则
Multi-Round Parsing-based Multiword Rules for Scientific OpenIE
摘要：
科学文献中的信息抽取（IE）促进了许多下游任务。OpenIE不需要任何关系模式，但识别一个关系短语来描述主体和客体之间的关系，是IE在科学领域的一个热门话题。主语、宾语和关系通常是多词表达式，这给在非常有限甚至没有训练数据的情况下识别表达式边界的方法带来了挑战。在这项工作中，我们提出了一套规则提取结构化信息的基础上依存句法分析，可以适用于任何科学数据集，不需要专家的注释。新数据集上的实验结果表明了该方法的有效性。我们也讨论了负面的结果。
英文摘要：
Information extraction (IE) in scientific literature has facilitated many down-stream tasks. OpenIE, which does not require any relation schema but identifies a relational phrase to describe the relationship between a subject and an object, is being a trending topic of IE in sciences. The subjects, objects, and relations are often multiword expressions, which brings challenges for methods to identify the boundaries of the expressions given very limited or even no training data. In this work, we present a set of rules for extracting structured information based on dependency parsing that can be applied to any scientific dataset requiring no expert's annotation. Results on novel datasets show the effectiveness of the proposed method. We discuss negative results as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
