@inproceedings{bolt-etal-2019-stylometry,
title = "A Stylometry Toolkit for {L}atin Literature",
author = "Bolt, Thomas J. and
Flynt, Jeffrey H. and
Chaudhuri, Pramit and
Dexter, Joseph P.",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3035",
doi = "10.18653/v1/D19-3035",
pages = "205--210",
abstract = "Computational stylometry has become an increasingly important aspect of literary criticism, but many humanists lack the technical expertise or language-specific NLP resources required to exploit computational methods. We demonstrate a stylometry toolkit for analysis of Latin literary texts, which is freely available at www.qcrit.org/stylometry. Our toolkit generates data for a diverse range of literary features and has an intuitive point-and-click interface. The features included have proven effective for multiple literary studies and are calculated using custom heuristics without the need for syntactic parsing. As such, the toolkit models one approach to the user-friendly generation of stylometric data, which could be extended to other premodern and non-English languages underserved by standard NLP resources.",
}
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<abstract>Computational stylometry has become an increasingly important aspect of literary criticism, but many humanists lack the technical expertise or language-specific NLP resources required to exploit computational methods. We demonstrate a stylometry toolkit for analysis of Latin literary texts, which is freely available at www.qcrit.org/stylometry. Our toolkit generates data for a diverse range of literary features and has an intuitive point-and-click interface. The features included have proven effective for multiple literary studies and are calculated using custom heuristics without the need for syntactic parsing. As such, the toolkit models one approach to the user-friendly generation of stylometric data, which could be extended to other premodern and non-English languages underserved by standard NLP resources.</abstract>
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%0 Conference Proceedings
%T A Stylometry Toolkit for Latin Literature
%A Bolt, Thomas J.
%A Flynt, Jeffrey H.
%A Chaudhuri, Pramit
%A Dexter, Joseph P.
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F bolt-etal-2019-stylometry
%X Computational stylometry has become an increasingly important aspect of literary criticism, but many humanists lack the technical expertise or language-specific NLP resources required to exploit computational methods. We demonstrate a stylometry toolkit for analysis of Latin literary texts, which is freely available at www.qcrit.org/stylometry. Our toolkit generates data for a diverse range of literary features and has an intuitive point-and-click interface. The features included have proven effective for multiple literary studies and are calculated using custom heuristics without the need for syntactic parsing. As such, the toolkit models one approach to the user-friendly generation of stylometric data, which could be extended to other premodern and non-English languages underserved by standard NLP resources.
%R 10.18653/v1/D19-3035
%U https://aclanthology.org/D19-3035
%U https://doi.org/10.18653/v1/D19-3035
%P 205-210
Markdown (Informal)
[A Stylometry Toolkit for Latin Literature](https://aclanthology.org/D19-3035) (Bolt et al., EMNLP-IJCNLP 2019)
ACL
- Thomas J. Bolt, Jeffrey H. Flynt, Pramit Chaudhuri, and Joseph P. Dexter. 2019. A Stylometry Toolkit for Latin Literature. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 205–210, Hong Kong, China. Association for Computational Linguistics.