impresso Text Reuse at Scale. An interface for the exploration of text reuse data in semantically enriched historical newspapers

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Marten Düring, Matteo Romanello, Maud Ehrmann, Kaspar Beelen, Daniele Guido, Brecht Deseure, Estelle Bunout, Jana Keck, Petros Apostolopoulos
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引用次数: 0

Abstract

Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts or to reveal evolving publication practices of historical media. This research is often supported by interactive visualizations which highlight relations and differences between text segments. In this paper, we build on earlier work in this domain. We present impresso Text Reuse at Scale, the to our knowledge first interface which integrates text reuse data with other forms of semantic enrichment to enable a versatile and scalable exploration of intertextual relations in historical newspaper corpora. The Text Reuse at Scale interface was developed as part of the impresso project and combines powerful search and filter operations with close and distant reading perspectives. We integrate text reuse data with enrichments derived from topic modeling, named entity recognition and classification, language and document type detection as well as a rich set of newspaper metadata. We report on historical research objectives and common user tasks for the analysis of historical text reuse data and present the prototype interface together with the results of a user evaluation.
大规模的文本重用。在语义丰富的历史报纸中探索文本重用数据的接口
文本重用揭示了大型语料库中文本的有意义的重复。人文学者使用文本再利用来研究,例如,有影响力的文本的后接受或揭示历史媒体不断发展的出版实践。这种研究经常得到交互式可视化的支持,它突出了文本段之间的关系和差异。在本文中,我们以该领域的早期工作为基础。我们提出了impresso大规模文本重用,这是我们的知识第一接口,它将文本重用数据与其他形式的语义丰富集成在一起,从而能够对历史报纸语料库中的互文关系进行通用和可扩展的探索。大规模文本重用界面是作为impresso项目的一部分开发的,它结合了强大的搜索和过滤操作以及近距离和远距离阅读视角。我们将文本重用数据与来自主题建模、命名实体识别和分类、语言和文档类型检测以及一组丰富的报纸元数据的丰富内容集成在一起。我们报告了历史研究目标和常见的用户任务,用于分析历史文本重用数据,并提供了原型界面以及用户评估结果。
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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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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