Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus

To show the importance of preparing historical editions as textual data first, while leaving presentation (whether in print or on the web) as a secondary down-stream task, this article identifies beneficial outcomes for research that can be achieved through computational analysis when such a corpus...

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Main Author: Jeffrey C. Witt
Format: Article
Language:fra
Published: Université de Lille 2024-10-01
Series:Methodos
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Online Access:https://journals.openedition.org/methodos/10987
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author Jeffrey C. Witt
author_facet Jeffrey C. Witt
author_sort Jeffrey C. Witt
collection DOAJ
description To show the importance of preparing historical editions as textual data first, while leaving presentation (whether in print or on the web) as a secondary down-stream task, this article identifies beneficial outcomes for research that can be achieved through computational analysis when such a corpus of textual data is at hand. With a focus on the deep intertextuality characteristic of the medieval scholastic corpus, it reviews three distinct methods for detecting different forms of textual relatedness within the corpus: n-gram intersections, document embeddings, and convolution. In each case, special attention is given to how the availability of a domain specific knowledge graph helps us both properly prepare the corpus for analysis and visualize the results in ways that enhance research. Such results include observing trends in citation practices across different genres and sub-genres of the corpus, automatically grouping questions by similarity, and detecting sustained and uncited textual re-use.
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spelling doaj-art-a796c48653b3426f917b1604a2dd7f8c2025-01-30T14:11:11ZfraUniversité de LilleMethodos1769-73792024-10-012410.4000/12xqlFinding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpusJeffrey C. WittTo show the importance of preparing historical editions as textual data first, while leaving presentation (whether in print or on the web) as a secondary down-stream task, this article identifies beneficial outcomes for research that can be achieved through computational analysis when such a corpus of textual data is at hand. With a focus on the deep intertextuality characteristic of the medieval scholastic corpus, it reviews three distinct methods for detecting different forms of textual relatedness within the corpus: n-gram intersections, document embeddings, and convolution. In each case, special attention is given to how the availability of a domain specific knowledge graph helps us both properly prepare the corpus for analysis and visualize the results in ways that enhance research. Such results include observing trends in citation practices across different genres and sub-genres of the corpus, automatically grouping questions by similarity, and detecting sustained and uncited textual re-use.https://journals.openedition.org/methodos/10987digital scholarly editionsscholasticismMiddle Agesintertextualityn-gramsembeddings
spellingShingle Jeffrey C. Witt
Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus
Methodos
digital scholarly editions
scholasticism
Middle Ages
intertextuality
n-grams
embeddings
title Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus
title_full Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus
title_fullStr Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus
title_full_unstemmed Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus
title_short Finding Relatedness: pathways for detecting textual relatedness in the medieval scholastic corpus
title_sort finding relatedness pathways for detecting textual relatedness in the medieval scholastic corpus
topic digital scholarly editions
scholasticism
Middle Ages
intertextuality
n-grams
embeddings
url https://journals.openedition.org/methodos/10987
work_keys_str_mv AT jeffreycwitt findingrelatednesspathwaysfordetectingtextualrelatednessinthemedievalscholasticcorpus