2020
DOI: 10.1017/jch.2020.23
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Creating, Linking, and Analyzing Chinese and Korean Datasets: Digital Text Annotation in MARKUS and COMPARATIVUS

Abstract: MARKUS, a multilingual digital text annotation and analysis platform, allows historians and other researchers to construct datasets from primary sources available to them in full-text digital format. Originally designed for those working with pre-twentieth-century Chinese texts, MARKUS has developed into a multifunctional annotation platform that is particularly suited for the automated annotation, referencing, and visualization of named entities in modern and literary Chinese and premodern Korean texts, but m… Show more

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Cited by 5 publications
(2 citation statements)
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“…Until recently, numerous digital humanities tools commonly applied NER to develop digital humanities tools to support digital humanities research. For example, the MARKUS text analysis platform developed by Leiden University in The Netherlands is an online text annotation tool that allows its users to upload text and select the type of annotation required, and the system will automatically annotate proprietary terms, such as people, places and official names, while also providing users with external references from the resources, such as Wikipedia, CBDB, TGAZ and ZDict (De Weerdt, 2020; Ho, 2015). Yohan et al (2014) proposed a rule-based NER and classification system that can automatically identify named entities in the text and provide them with appropriate categories corresponding to the Telugu language.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Until recently, numerous digital humanities tools commonly applied NER to develop digital humanities tools to support digital humanities research. For example, the MARKUS text analysis platform developed by Leiden University in The Netherlands is an online text annotation tool that allows its users to upload text and select the type of annotation required, and the system will automatically annotate proprietary terms, such as people, places and official names, while also providing users with external references from the resources, such as Wikipedia, CBDB, TGAZ and ZDict (De Weerdt, 2020; Ho, 2015). Yohan et al (2014) proposed a rule-based NER and classification system that can automatically identify named entities in the text and provide them with appropriate categories corresponding to the Telugu language.…”
Section: Literature Reviewmentioning
confidence: 99%
“…While the annotation data is stored using an XML representation which can be directly edited, most annotations are created by using a point-and-click interface (Fig. 1), which builds upon interface design approaches used in existing annotation systems such as Recogito [2] and MARKUS [3]. Sections of text are loaded from the digital library using its Application Programming Interface (API) and displayed in the main part of the screen.…”
Section: A Entity Annotationmentioning
confidence: 99%