Quantitative Authorship Attribution: An Evaluation of Techniques

By Jack Grieve. Literary and Linguistic Computing, 2007

394 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f43816f98287ebcd0d7355917958bcb4500305fd

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In Artificial Intelligence

Citations in the corpus

On the features of translationese (2015, methodology): “We therefore use three different type-token ratio (TTR) measures, following Grieve (2007), where V is the number of types and N is the number of tokens per chunk.”
Syntactic N-grams as machine learning features for natural language processing (2014, background): “Until now, a number of studies (Grieve, 2007; Keselj, Peng, Cercone, & Thomas, 2003; Luyckx, 2010) have shown that the most effective measures are lexical and character features.”
Comparative study on methods of detecting research fronts using different types of citation (2009, methodology): “Likewise, a recent comparison of different lexical and character features on the same evaluation corpora (Grieve, 2007) showed that character n-grams were the most effective measures (outperformed in the specific experiments only by a combination of frequent words and punctuation marks).”
A survey of modern authorship attribution methods (2009, methodology): “Likewise, a recent comparison of different lexical and character features on the same evaluation corpora (Grieve, 2007) showed that character n-grams were the most effective measures (outperformed in the specific experiments only by a combination of frequent words and punctuation marks).”
Computational Sociolinguistics: A Survey (2016, background): “salization) to form sociolects is weaker than usually assumed. The unique use of language by an individual is an idiolect, and this concept is in particular relevant for authorship attribution (e.g., Grieve (2007)). Recognizing thatlanguageusecanrevealsocialpatterns,manystudies in computationallingu”
Computational methods in authorship attribution (2009, neutral): “Recently, Grieve (2007) has run an exhaustive battery of tests using this type of method.”
Topic or Style? Exploring the Most Useful Features for Authorship Attribution (2018, methodology): “Previous work on feature exploration for authorship at-tribution, focused on the overall effectiveness of features without considering the characteristics of the datasets to which they were applied, e.g. (Grieve, 2007; Guthrie, 2008; Stamatatos, 2009; Brennan et al., 2012; Sapkota et al., 2015).”
Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework (2021, background): “Especially in authorship attribution, recurring style features include character n-grams, punctuation, average word length or function word frequency (Neal et al., 2017; Grieve, 2007; Stamatatos, 2009).”