Increasing Recall for Text Re-use in Historical Documents to Support Research in the Humanities

Büchler, Marco; Crane, Gregory; Moritz, Maria; Babeu, Alison

doi:10.1007/978-3-642-33290-6_11

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…In the text mining literature, there has been a huge volume of work on detecting similarity between or among texts (e.g. [SS95, CGPW97, FEC05, BCMB12]). Detection techniques often have to address particular requirements related to a specific context, such as languages, periods of articles, genres of articles, and so on.…”

Section: Introductionmentioning

confidence: 99%

Constructive Visual Analytics for Text Similarity Detection

Abdul‐Rahman

Roe

Olsen

et al. 2016

Computer Graphics Forum

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Detecting similarity between texts is a frequently encountered text mining task. Because the measurement of similarity is typically composed of a number of metrics, and some measures are sensitive to subjective interpretation, a generic detector obtained using machine learning often has difficulties balancing the roles of different metrics according to the semantic context exhibited in a specific collection of texts. In order to facilitate human interaction in a visual analytics process for text similarity detection, we first map the problem of pairwise sequence comparison to that of image processing, allowing patterns of similarity to be visualized as a 2D pixelmap. We then devise a visual interface to enable users to construct and experiment with different detectors using primitive metrics, in a way similar to constructing an image processing pipeline. We deployed this new approach for the identification of commonplaces in 18th‐century literary and print culture. Domain experts were then able to make use of the prototype system to derive new scholarly discoveries and generate new hypotheses.

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Section: Introductionmentioning

confidence: 99%

Constructive Visual Analytics for Text Similarity Detection

Abdul‐Rahman

Roe

Olsen

et al. 2016

Computer Graphics Forum

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“…Cf. https://www.etrap.eu/research/tracer/ (eTRAP Project, University of Göttingen) andBüchler (2013) andBüchler et al (2012).3 Cf. https://artfl-project.uchicago.edu/text-pair (ARTFL Project, University of Chicago) andAllen et al (2010) andHorton et al (2010).…”

mentioning

confidence: 99%

On Digital Comparative Editions and Textual Similarity Detection Tools: Towards a Hypertextual Cartography of a Rewritten Myth

Suchecka¹,

Gasiglia²

2022

Tackling the Toolkit: Plotting Poetry Through Computational Literary Studies

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Our project aims to expose the intertextual relationships observable within a heterogeneous literary corpus. For this purpose, we examine the output of two text reuse detection tools, Tracer and TextPAIR. We suggest some solutions to overcome the specific limitations observed in those tools and to enhance data quality. We believe that automatic analysis of the rewriting process can make it more comprehensible if the analysis is combined with empirical research methods adapted to the corpus in question.

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“…Specifically, he uses a fingerprinting approach by selecting certain ngrams from an upfront presegmentized corpus. Furthermore, focusing on high recall, the detection of Homeric quotations in Athenaeus' Deipnosophistai is investigated by Büchler et al (2012), searching for distinctive words within reuse. Efforts to automatically process ancient texts are also made around the Perseus Digital Library project (Crane, 1985).…”

Section: Text Reuse Detection In Historical Textmentioning

confidence: 99%

“…The computational detection of such passed, reused text in the form of historical text reuseincluding (verbatim) quotations, allusions, the unintended reuse of a saying, or even cases of cross-linguistic reuse in the form of translations-can be applied in many respects. It can help tracing down historical content (a.k.a., lines of transmission), which is essential to the field of textual criticism (Büchler et al, 2012), or it can help assigning a text to an author (Gupta & Lehal, 2009;Steyvers et al, 2004) if the original author is not clear. In the context of massive digitization projects, text reuse detection can identify relationships between text excerpts referring to the same source.…”

Section: Introductionmentioning

confidence: 99%

Modification Analysis in Historical Paraphrastical Parallel Text

Berger¹

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Clarifying the genesis of a passed down text is of outmost importance for many scholarly disciplines within the humanities such as history, literary studies, and Bible studies. The computational detection of such passed down texts in the form of historical text reuse, including citations, quotations or allusions, unintended reuse of a saying, or even of cross-linguistic reuse in the form of translations, can be applied in many respects. It can help tracing down historical content (a.k.a., lines of transmission), which is essential to the field of textual criticism. In modern literature it can help assigning text to authors. In the context of massive digitization List of publications included in the thesis 2018

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Increasing Recall for Text Re-use in Historical Documents to Support Research in the Humanities

Cited by 8 publications

References 6 publications

Constructive Visual Analytics for Text Similarity Detection

Constructive Visual Analytics for Text Similarity Detection

On Digital Comparative Editions and Textual Similarity Detection Tools: Towards a Hypertextual Cartography of a Rewritten Myth

Modification Analysis in Historical Paraphrastical Parallel Text

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