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Digital Scholarship in the Humanities is a peer-reviewed journal published by Oxford University Press, covering Natural Language Processing Techniques, Digital Humanities and Scholarship, Authorship Attribution and Profiling.
| Publisher | Oxford University Press |
|---|---|
| Reference style | Author–year (OUP) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Digital Scholarship in the Humanities, 12(3), pp. 45–58.
Formats any DOI in the closest standard style — Digital Scholarship in the Humanities has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Natural Language Processing Techniques Digital Humanities and Scholarship Authorship Attribution and Profiling Topic Modeling Computational and Text Analysis Methods |
| ISSN | 2055-7671 |
| Citation impact (2-yr) | 0.96 |
| h-index | 42 |
| i10-index | 250 |
| Total citations | 9,752 |
| Article processing charge | $3,093 |
| Top institutions publishing here | King's College London |
| Journal website | dsh.oxfordjournals.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
This article is concerned with the data structures, properties of query languages, and visualization facilities required for the generic representation of richly annotated, heterogeneous linguistic corpora. We propose that above and beyond a general graph-based data model, which is becoming increasingly popular in many complex annotation formats, a well-defined concept of multiple, potentially conflicting segmentation…
This article builds on a mathematical explanation of one the most prominent stylometric measures, Burrows’s Delta (and its variants), to understand and explain its working. Starting with the conceptual separation between feature selection, feature scaling, and distance measures, we have designed a series of controlled experiments in which we used the kind of feature scaling…
The aim of this study is to find such a minimal size of text samples for authorship attribution that would provide stable results independent of random noise. A few controlled tests for different sample lengths, languages, and genres are discussed and compared. Depending on the corpus used, the minimal sample length varied from 2,500 words…
Much research in translation studies indicates that translated texts are ontologically different from original non-translated ones. Translated texts, in any language, can be considered a dialect of that language, known as ‘translationese’. Several characteristics of translationese have been proposed as universal in a series of hypotheses. In this work, we test these hypotheses using a…
Finding out whether a word occurs significantly more often in one text or corpus than in another is an important question in analysing corpora. As noted by Kilgarriff (Language is never, ever, ever, random, Corpus Linguistics and Linguistic Theory , 2005; 1(2): 263–76.), the use of the χ 2 and log-likelihood ratio tests is problematic…
