@inproceedings{ryskina-etal-2017-automatic,
title = "Automatic Compositor Attribution in the First Folio of Shakespeare",
author = "Ryskina, Maria and
Alpert-Abrams, Hannah and
Garrette, Dan and
Berg-Kirkpatrick, Taylor",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2065",
doi = "10.18653/v1/P17-2065",
pages = "411--416",
abstract = "Compositor attribution, the clustering of pages in a historical printed document by the individual who set the type, is a bibliographic task that relies on analysis of orthographic variation and inspection of visual details of the printed page. In this paper, we introduce a novel unsupervised model that jointly describes the textual and visual features needed to distinguish compositors. Applied to images of Shakespeare{'}s First Folio, our model predicts attributions that agree with the manual judgements of bibliographers with an accuracy of 87{\%}, even on text that is the output of OCR.",
}
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%0 Conference Proceedings
%T Automatic Compositor Attribution in the First Folio of Shakespeare
%A Ryskina, Maria
%A Alpert-Abrams, Hannah
%A Garrette, Dan
%A Berg-Kirkpatrick, Taylor
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F ryskina-etal-2017-automatic
%X Compositor attribution, the clustering of pages in a historical printed document by the individual who set the type, is a bibliographic task that relies on analysis of orthographic variation and inspection of visual details of the printed page. In this paper, we introduce a novel unsupervised model that jointly describes the textual and visual features needed to distinguish compositors. Applied to images of Shakespeare’s First Folio, our model predicts attributions that agree with the manual judgements of bibliographers with an accuracy of 87%, even on text that is the output of OCR.
%R 10.18653/v1/P17-2065
%U https://aclanthology.org/P17-2065
%U https://doi.org/10.18653/v1/P17-2065
%P 411-416
Markdown (Informal)
[Automatic Compositor Attribution in the First Folio of Shakespeare](https://aclanthology.org/P17-2065) (Ryskina et al., ACL 2017)
ACL
- Maria Ryskina, Hannah Alpert-Abrams, Dan Garrette, and Taylor Berg-Kirkpatrick. 2017. Automatic Compositor Attribution in the First Folio of Shakespeare. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 411–416, Vancouver, Canada. Association for Computational Linguistics.