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Information extraction of extend relation in scientific papers

机译:科学论文中延伸关系的信息提取

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Extend relation is one of the papers' relations obtained by using the citation-context based approach where this approach can identify more varied papers relations than two other approaches i.e. content-based and citation-analysis. However, the recent research with citation-context based approach only focuses on identification of relations, and cannot give out information related to this relation in more detail. The information of relation is very necessary for researchers in developing a research. In this paper, the scheme of information extraction process for extend relations is proposed which covers the classification process of extend categories and information extraction process based on the classification of extend categories. The experimental results show that the most optimal process in extend information extraction is reached by classifying extend categories from all sentences directly. The combination between the best extend categories classification and the proposed regular expression for relations information extraction delivered F-Measure more than 50%. This result can be said as good by considering that the proportion of extend sentence in the corpus is very small. The other experiment also shows that the optimal rule is reached for Data and Tool category.
机译:延伸关系是通过使用基于引文的基于引文的方法获得的论文关系之一,其中这种方法可以识别比另外两种方法更具不同的纸张关系,即基于内容和引文分析。然而,最近通过基于引文的方法的研究仅侧重于识别关系,并且不能更详细地泄露与这一关系有关的信息。研究人员在开发研究方面是非常必要的。在本文中,提出了延长关系的信息提取过程方案,其涵盖了基于扩展类别分类的扩展类别和信息提取过程的分类过程。实验结果表明,通过直接分类所有句子的扩展类别,达到了扩展信息提取中最佳的过程。最佳扩展类别分类与建议的正则表达式之间的组合信息提取传递F-Meacer超过50 %。考虑到语料库中的延伸句子的比例非常小,可以说这一结果可以说。另一个实验还显示了对数据和工具类别达到最佳规则。

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