A Widow and her Soldier: Stylometry and the American Civil War

Holmes, David; Gordon, Lesley J.; Wilson, Christine

doi:10.1093/llc/16.4.403

Cited by 45 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies since that of Mosteller and Wallace have shown the efficacy of function words for authorship attribution in different scenarios (Morton 1978;Burrows 1987;Karlgren & Cutting 1994;Merriam & Matthews 1994;Kessler et al 1997;Argamon et al 1998;Holmes 1998;de Vel et al 2001;Holmes et al 2001aHolmes et al , 2001bBaayen et al 2002;Binongo 2003;Juola & Baayen 2003;Zhao & Zobel 2005;Argamon & Levitan 2005;Koppel et al 2005Koppel et al , 2006a, confirming the hypothesis that different authors tend to have different characteristic patterns of function word use.…”

Section: Function Wordsmentioning

confidence: 82%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

462

278

View full text Add to dashboard Cite

Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample.In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.

show abstract

Section: Function Wordsmentioning

confidence: 82%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

462

278

View full text Add to dashboard Cite

show abstract

“…This was not demonstrated using complex statistical techniques; rather, function word frequencies were tallied, the authors' tallies compared, PCA used to reduce the dimensionality of the data, and the resulting plots inspected: the two authors' works form obvious clusters. Similar procedures can be found in (Holmes & Forsyth 1995, Holmes et al 2001, and Peng & Hentgartner 2002.…”

Section: Principal Component Analysismentioning

confidence: 74%

Principal Component Analysis for Authorship Attribution

Jamak¹

2012

scjournal

View full text Add to dashboard Cite

A common problem in statistical pattern recognition is that of feature selection or feature extraction. Feature selection refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. In this paper the data collected by counting words and characters in around a thousand paragraphs of each sample book underwent a principal component analysis performed using neural networks. Then first of the principal components is used to distinguished the books authored by a certain author.

show abstract

“…This was not demonstrated using complex statistical techniques; rather, function word frequencies were tallied, the authors' tallies compared, PCA used to reduce the dimensionality of the data, and the resulting plots inspected: the two authors' works form obvious clusters. Similar procedures can be found in (Bosh and Smith 1998, Holmes et al 2001, Peng, and Hengartner 2002.…”

Section: Principal Component Analysismentioning

confidence: 86%

Authorship Attribution Using Principal Component Analysis and Nearest Neighbor Rule for Neural Networks

Can

2012

scjournal

View full text Add to dashboard Cite

Abstract-Feature extraction is a common problem in statistical pattern recognition. It refers to a process whereby a data space is transformed into a feature space that, in theory, has exactly the same dimension as the original data space. However, the transformation is designed in such a way that the data set may be represented by a reduced number of "effective" features and yet retain most of the intrinsic information content of the data; in other words, the data set undergoes a dimensionality reduction. Principal component analysis is one of these processes. In this paper the data collected by counting selected syntactic characteristics in around a thousand paragraphs of each of the sample books underwent a principal component analysis. To make a comparison, the original data is also processed. Authors of texts identified with higher success by the competitive neural networks, which use principal components. The process repeated on another group of authors, and similar results are obtained.

show abstract

A Widow and her Soldier: Stylometry and the American Civil War

Cited by 45 publications

References 0 publications

Computational methods in authorship attribution

Computational methods in authorship attribution

Principal Component Analysis for Authorship Attribution

Authorship Attribution Using Principal Component Analysis and Nearest Neighbor Rule for Neural Networks

Contact Info

Product

Resources

About