论文标题
使用bibtex自动生成标记的数据进行引用字段提取
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
论文作者
论文摘要
准确解析引文参考字符串对于自动构建学术数据库（例如Google Scholar或语义学者）至关重要。引用场提取（CFE）正是此任务 - 给定一个参考标签，该参考标签是指代币，地点，标题，编辑，期刊，期刊，页面等。大多数CFE的方法都受到监督，并依靠与各种参考格式相比的标签数据集中的培训。 Bibtex是广泛使用的参考管理工具，它提供了一种自然的方法，可以自动生成和标签CFE的培训数据。在本文中，我们描述了一种使用bibtex自动生成的技术，它是一个大尺度的41m标记字符串），标记的数据集，该数据集比当前最大的CFE数据集大四个数量级，即UMass Citation Field Field Field field DataSet [Anzaroot and McCalloom and McCallum，2013年]。我们通过实验证明了如何使用基于罗伯塔的[Liu等，2019]模型使用数据集来提高UMass CFE的性能。与以前的SOTA相比，我们达到了24.48％的相对误差降低，达到96.3％的跨度F1得分。
Accurate parsing of citation reference strings is crucial to automatically construct scholarly databases such as Google Scholar or Semantic Scholar. Citation field extraction (CFE) is precisely this task---given a reference label which tokens refer to the authors, venue, title, editor, journal, pages, etc. Most methods for CFE are supervised and rely on training from labeled datasets that are quite small compared to the great variety of reference formats. BibTeX, the widely used reference management tool, provides a natural method to automatically generate and label training data for CFE. In this paper, we describe a technique for using BibTeX to generate, automatically, a large-scale 41M labeled strings), labeled dataset, that is four orders of magnitude larger than the current largest CFE dataset, namely the UMass Citation Field Extraction dataset [Anzaroot and McCallum, 2013]. We experimentally demonstrate how our dataset can be used to improve the performance of the UMass CFE using a RoBERTa-based [Liu et al., 2019] model. In comparison to previous SoTA, we achieve a 24.48% relative error reduction, achieving span level F1-scores of 96.3%.
