使用ACM DL纸张元数据作为建立教育馆藏的辅助来源

Yinlin Chen, E. Fox
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引用次数: 9

摘要

一些数字图书馆从多个内容提供者处获取元数据记录,以构建其馆藏。然而,这类元数据记录的质量和数量受到所获取内容的限制。为了确保收集的增长,并将范围扩大到可以收获的范围之外,我们需要额外的内容获取方法。因此,我们讨论了Ensemble项目(NSDL中的路径努力)如何在机器学习的帮助下扩大其集合。由于Ensemble旨在帮助计算机教育,我们利用ACM数字图书馆记录作为帮助迁移学习的资源。我们已经建立了分类器,可以识别潜在的额外资源是否与计算教育有关。我们将其作为一个跨域文本分类问题来处理,并开发了用于分类器训练的特征提取和自举的合适方法。我们在三个计算教育元数据记录的数据集上的实验表明,我们的方法可以提高添加到Ensemble中的记录的质量和数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using ACM DL paper metadata as an auxiliary source for building educational collections
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.
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