Network motifs for translator stylometry identification.
Despite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguis...
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doaj-6642a72605e34ddb9449a394f775d2512021-03-03T20:53:53ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01142e021180910.1371/journal.pone.0211809Network motifs for translator stylometry identification.Heba El-FiqiEleni PetrakiHussein A AbbassDespite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguistics. In a two stage process, this paper first evaluates the use of existing lexical measures for the translator stylometry problem. Similar to previous research we found that using vocabulary richness in its traditional form as it has been used in the literature could not identify translator stylometry. This encouraged us to design an approach with the aim of identifying the distinctive patterns of a translator by employing network-motifs. Networks motifs are small sub-graphs which aim at capturing the local structure of a complex network. The proposed approach achieved an average accuracy of 83% in three-way classification. These results demonstrate that classic tools based on lexical features can be used for identifying translator stylometry if they get augmented with appropriate non-parametric scaling. Moreover, the use of complex network analysis and network motifs mining provided made it possible to design features that can solve translator stylometry analysis problems.https://doi.org/10.1371/journal.pone.0211809 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Heba El-Fiqi Eleni Petraki Hussein A Abbass |
spellingShingle |
Heba El-Fiqi Eleni Petraki Hussein A Abbass Network motifs for translator stylometry identification. PLoS ONE |
author_facet |
Heba El-Fiqi Eleni Petraki Hussein A Abbass |
author_sort |
Heba El-Fiqi |
title |
Network motifs for translator stylometry identification. |
title_short |
Network motifs for translator stylometry identification. |
title_full |
Network motifs for translator stylometry identification. |
title_fullStr |
Network motifs for translator stylometry identification. |
title_full_unstemmed |
Network motifs for translator stylometry identification. |
title_sort |
network motifs for translator stylometry identification. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2019-01-01 |
description |
Despite the extensive literature investigating stylometry analysis in authorship attribution research, translator stylometry is an understudied research area. The identification of translator stylometry contributes to many fields including education, intellectual property rights and forensic linguistics. In a two stage process, this paper first evaluates the use of existing lexical measures for the translator stylometry problem. Similar to previous research we found that using vocabulary richness in its traditional form as it has been used in the literature could not identify translator stylometry. This encouraged us to design an approach with the aim of identifying the distinctive patterns of a translator by employing network-motifs. Networks motifs are small sub-graphs which aim at capturing the local structure of a complex network. The proposed approach achieved an average accuracy of 83% in three-way classification. These results demonstrate that classic tools based on lexical features can be used for identifying translator stylometry if they get augmented with appropriate non-parametric scaling. Moreover, the use of complex network analysis and network motifs mining provided made it possible to design features that can solve translator stylometry analysis problems. |
url |
https://doi.org/10.1371/journal.pone.0211809 |
work_keys_str_mv |
AT hebaelfiqi networkmotifsfortranslatorstylometryidentification AT elenipetraki networkmotifsfortranslatorstylometryidentification AT husseinaabbass networkmotifsfortranslatorstylometryidentification |
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