The FairyNet Corpus - Character Networks for German Fairy Tales

David Schmidt, Albin Zehe, Janne Lorenzen, Lisa Sergel, Sebastian Düker, Markus Krug, F. Puppe
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引用次数: 4

Abstract

This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.
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童话网语料库——德国童话的人物网络
本文提出了一个德国童话故事数据集,用字符网络手工标注,获得了高度一致性的字符网络。这个语料库的发布提供了一个训练和比较不同的字符网络提取算法的机会,由于以前的研究人员的兴趣不同,到目前为止,这几乎是不可能的。我们通过为字符网络的自动提取提供基线实验,应用基于规则的管道和神经方法来证明我们的数据集的实用性,并发现神经方法在大多数评估设置中优于规则方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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