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Using ACM DL paper metadata as an auxiliary source for building educational collections

机译:使用ACM DL纸质元数据作为构建教育收藏的辅助资源

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Some digital libraries harvest metadata records from multiple content providers to build their collections. However, the quality and quantity of such metadata records are limited by what is harvested. To ensure collection growth, and to expand the scope beyond just what can be harvested, additional content acquisition methods are needed. Accordingly, we discuss how the Ensemble project (a pathway effort in the NSDL) is broadening its collection with the help of machine learning. Since Ensemble aims to aid computing education, we make use of ACM Digital Library records as a resource to help with transfer learning. We have built classifiers that can identify if a potential additional resource is about computing education. We approached this as a cross-domain text classification problem and developed suitable methods for feature extraction and bootstrapping for classifier training. Our experiments on three datasets of computing education metadata records show our approach can enhance the quality and quantity of records being added to Ensemble.
机译:一些数字图书馆从多个内容提供商那里获取元数据记录,以建立其馆藏。但是,此类元数据记录的质量和数量受到所收获内容的限制。为了确保馆藏的增长,并将范围扩大到可以收获的范围之外,还需要其他内容获取方法。因此,我们讨论了Ensemble项目(NSDL中的一项途径工作)如何借助机器学习来扩展其集合。由于Ensemble旨在帮助计算机教育,因此我们将ACM数字图书馆记录用作资源来帮助进行转移学习。我们建立了分类器,可以识别潜在的其他资源是否与计算机教育有关。我们将其作为跨域文本分类问题来解决,并开发了适用于特征提取和自举的分类器训练方法。我们对计算教育元数据记录的三个数据集进行的实验表明,我们的方法可以提高添加到Ensemble的记录的质量和数量。

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