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Automatic Annotation of Bibliographical References for Descriptive Language Materials

机译:描述性语言材料的书目参考文献的自动注释

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The present paper considers the problem of annotating bibliographical references with labels/classes, given training data of references already annotated with labels. The problem is an instance of document categorization where the documents are short and written in a wide variety of languages. The skewed distributions of title words and labels calls for special carefulness when choosing a Machine Learning approach. The present paper describes how to induce Disjunctive Normal Form formulae (DNFs), which have several advantages over Decision Trees. The approach is evaluated on a large real-world collection of bibliographical references.
机译:给定已经用标签注释的参考文献的训练数据,本文考虑用标签/类注释书目参考文献的问题。问题是文档分类的一个实例,其中文档简短且用多种语言编写。选择机器学习方法时,标题词和标签的偏斜分布需要特别小心。本文介绍了如何推导析取范式公式(DNFs),它比决策树更具优势。该方法是在大量的真实参考文献中进行评估的。

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