'Delta': a Measure of Stylistic Difference and a Guide to Likely Authorship
By J. Burrows. Literary and Linguistic Computing, 2002
615 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/1081e505f03a9ae0dc3d55881b59a67e264f2d1f
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In Artificial Intelligence
- Jonathan Schler
- Efstathios Stamatatos
- Jordan L. Boyd-Graber
- Moshe Koppel
- Yuening Hu
- Shlomo Engelson Argamon
- David Mimno
- Tomas Krilavičius
Citations in the corpus
- Stylometric Analysis of Parliamentary Speeches: Gender Dimension (2017, methodology): “…2013b; Rybicki and Eder, 2011; Eder and Rybicki, 2013; Eder, 2013a) (usually, they coincide with function words (Hochmann et al., 2010; Sigurd et al., 2004)), as features, because they are considered to be topicneutral and perform well (Juola and Baayen, 2005; Holmes et al., 2001; Burrows, 2002).”
- Applications of Topic Models (2017, background): “Therefore, measures such as Burrows’ delta [Burrows, 2002] restrict attention to the most frequent words in a corpus.”
- Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “, attributing anonymous or disputed literary works to known authors) (Burrows, 2002; Hoover, 2004a).”
- A survey of modern authorship attribution methods (2009, methodology): “, attributing anonymous or disputed literary works to known authors) (Burrows, 2002; Hoover, 2004a).”
- Class-based N-gram language difference models for data selection (2015, background): “In particular, [8] uses the difference in word frequencies across authors, genres, or eras.”
- Computational methods in authorship attribution (2009, positive): “One such method is Burrows's (2002a) Delta, which has been extended and used for a variety of attribution problems (Burrows 2002b; Hoover 2004a, 2004b), and is equivalent to an approximate probabilistic ranking based on a multidimensional Laplacian distribution over frequently appearing words…”
- Narrative Fingerprints: Multi-Scale Author Identification via Novelty Curve Dynamics (2026, unknown): “The modern era of computational authorship attribution begins with Burrows (2002), who showed that function-word frequencies form effective author signatures.”