Understanding and explaining Delta measures for authorship attribution

Evert, Stefan; Proisl, Thomas; Jannidis, Fotis; Reger, Isabella; Pielström, Steffen; Schöch, Christof; Vitt, Thorsten

doi:10.1093/llc/fqx023

Cited by 124 publications

(52 citation statements)

References 12 publications

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“…The resulting clusters of individual languages are displayed in a dendrogram. We used an agglomerative clustering method very popular in authorship attribution and stylometric studies [25] based on the Delta measure [26] and Ward linkage method [27].…”

Section: Language Clusteringmentioning

confidence: 99%

Measuring Language Distance of Isolated European Languages

Gamallo

Campos²,

Alegria

2020

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Phylogenetics is a sub-field of historical linguistics whose aim is to classify a group of languages by considering their distances within a rooted tree that stands for their historical evolution. A few European languages do not belong to the Indo-European family or are otherwise isolated in the European rooted tree. Although it is not possible to establish phylogenetic links using basic strategies, it is possible to calculate the distances between these isolated languages and the rest using simple corpus-based techniques and natural language processing methods. The objective of this article is to select some isolated languages and measure the distance between them and from the other European languages, so as to shed light on the linguistic distances and proximities of these controversial languages without considering phylogenetic issues. The experiments were carried out with 40 European languages including six languages that are isolated in their corresponding families: Albanian, Armenian, Basque, Georgian, Greek, and Hungarian.

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Section: Language Clusteringmentioning

confidence: 99%

Measuring Language Distance of Isolated European Languages

Gamallo

Campos²,

Alegria

2020

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“…Evert et al [26] point out how Burrows' Delta is a distance measure, i.e., "it describes the distance between one text and a group of texts". Hence, the smaller the Delta score, the smaller the distance between the texts, and the more similar these texts are stylistically.…”

Section: Syntactic Divergencementioning

confidence: 99%

“…Hence, the smaller the Delta score, the smaller the distance between the texts, and the more similar these texts are stylistically. We will now briefly explain how Burrows' Delta is calculated, but, for a more extensive explanation and formulae, refer to the work of Burrows himself [12] and Evert et al [26]. In short, in order to compare one text (text a) to a corpus consisting of various other texts (texts b and c), the n most frequent words from that corpus are collected.…”

Section: Syntactic Divergencementioning

confidence: 99%

Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics

et al. 2020

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Due to the growing success of neural machine translation (NMT), many have started to question its applicability within the field of literary translation. In order to grasp the possibilities of NMT, we studied the output of the neural machine system of Google Translate (GNMT) and DeepL when applied to four classic novels translated from English into Dutch. The quality of the NMT systems is discussed by focusing on manual annotations, and we also employed various metrics in order to get an insight into lexical richness, local cohesion, syntactic, and stylistic difference. Firstly, we discovered that a large proportion of the translated sentences contained errors. We also observed a lower level of lexical richness and local cohesion in the NMTs compared to the human translations. In addition, NMTs are more likely to follow the syntactic structure of a source sentence, whereas human translations can differ. Lastly, the human translations deviate from the machine translations in style.

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“…Third, different distance-based measures have been suggested. As well-known strategies, one can mention Burrows' Delta (Burrows, 2002;Evert et al, 2017) using the top m most frequent word-tokens (with m = 40 to 1,000), the Kullback-Leibler divergence (Zhao & Zobel, 2007) using a predefined set of 363 English words, or Labbé's method (Labbé, 2014) based on the entire vocabulary and opting for a variant of the Tanimoto distance, an approach found effective for Authorship Attribution (AA; Kocher & Savoy, 2017b).…”

Section: State Of the Artmentioning

confidence: 99%

Authorship of Pauline epistles revisited

Savoy

2019

Asso for Info Science & Tech

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The name Paul appears in 13 epistles, but is he the real author? According to different biblical scholars, the number of letters really attributed to Paul varies from 4 to 13, with a majority agreeing on seven. This article proposes to revisit this authorship attribution problem by considering two effective methods (Burrows' Delta, Labbé's intertextual distance). Based on these results, a hierarchical clustering is then applied showing that four clusters can be derived, namely: {Colossians‐Ephesians}, {1 and 2 Thessalonians}, {Titus, 1 and 2 Timothy}, and {Romans, Galatians, 1 and 2 Corinthians}. Moreover, a verification method based on the impostors' strategy indicates clearly that the group {Colossians‐Ephesians} is written by the same author who seems not to be Paul. The same conclusion can be found for the cluster {Titus, 1 and 2 Timothy}. The Letter to Philemon stays as a singleton, without any close stylistic relationship with the other epistles. Finally, a group of four letters {Romans, Galatians, 1 and 2 Corinthians} is certainly written by the same author (Paul), but the verification protocol also indicates that 2 Corinthians is related to 1 Thessalonians, rendering a clear and simple interpretation difficult.

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Understanding and explaining Delta measures for authorship attribution

Cited by 124 publications

References 12 publications

Measuring Language Distance of Isolated European Languages

Measuring Language Distance of Isolated European Languages

Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics

Authorship of Pauline epistles revisited

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