Bigrams of Syntactic Labels for Authorship Discrimination of Short Texts
Abstract
We present a method for authorship discrimination that is based on the frequency of bigrams of syntactic labels that arise from partial parsing of the text. We show that this method, alone or combined with other classification features, achieves a high accuracy on discrimination of the work of Anne and Charlotte Brontë, which is very difficult to do by traditional methods. Moreover, high accuracies are achieved even on fragments of text little more than 200 words long.
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Factors influencing the performance of existing authorship attribution models include the size of the training text, the number of candidate authors, and the imbalanced distribution of training texts among candidate authors.
“Factors influencing the performance of existing authorship attribution models include the size of the training text [Hirst and Feiguina, 2007;Marton et al., 2005], the number of candidate authors [Koppel et al., 2006], and the imbalanced distribution of training texts among candidate authors [Stamatatos, 2008].”
From Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges (2025) · cites this paper as Hirst and Feiguina, 2007
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Computational methods in authorship attribution
Koppel, M., Schler, J., & Argamon, S. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20961 · Cited by 528 · see citing works
Authorship Attribution for Social Media Forensics
Rocha, A., Scheirer, W. J., Forstall, C. W., Cavalcante, T., Theophilo, A., Shen, B., Carvalho, A. R. B., & Stamatatos, E. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2603960 · Cited by 222 · see citing works
Does Size Matter? Authorship Attribution, Small Samples, Big Problem
Eder, M. (2015) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqt066 · Cited by 214 · see citing works
Continuous user authentication using multi-modal biometrics
Saevanee, H., Clarke, N., Furnell, S., & Biscione, V. (2015) · Computers & Security · doi:10.1016/j.cose.2015.06.001 · Cited by 109 · see citing works
Eder, M. (2016) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqv010 · Cited by 68 · see citing works
Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges
Huang, B., Chen, C., & Shu, K. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3715073.3715076 · Cited by 42 · see citing works
“…the performance of existing authorship attri-bution models include the size of the training text (Hirst and Feigu-ina, 2007, Marton et al., 2005), the number of candidate authors (Koppel et al., 2006), and the imbalanced distribution of training texts among the candidate authors (Stamatatos, 2008).”
- Authors
- Hirst, G., & Feiguina, O.
- Affiliations
- University of Toronto (CA)
- ORCID
- Hirst, G.
- Venue
- Literary and Linguistic Computing
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-09-21
- Volume
- 22
- Issue
- 4
- Pages
- 405-417
- ISSN
- 0268-1145 (ISSN-L)
- 1477-4615
- OpenAlex
- W2025882237
- Cited by
- 227
- References
- 0
- Citations by year
- 2026: 1 · 2024: 6 · 2023: 8 · 2022: 8 · 2021: 6 · 2020: 25 · 2019: 12 · 2018: 12 · 2017: 11 · 2016: 18 · 2015: 23 · 2014: 11 · 2013: 12 · 2012: 53
- FWCI
- 5.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Literary and Linguistic Computing (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
- Keywords
- authorship attribution, bigrams, partial parsing, short text, Brontë sisters, literary style
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