And then there were none: Winnowing the Shakespeare claimants

Elliott, Ward E. Y.; Valenza, Robert J.

doi:10.1007/bf00055107

Cited by 81 publications

(6 citation statements)

References 13 publications

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“…Other applications of stylometry by other scholars, among them Elliot and Valenza [35,36,37] McDonald Jackson [68] and Brian Vickers [148] have uncovered other evidence refuting the Shakespearean attribution, and (perhaps more seriously) suggesting substantial mishandling of the Elegy in the course of Foster's analysis. Their analysis and debate, conducted for several years in the pages of Computers and the Humanities, eventually was able to establish to the satisfaction of all parties [108,148] that the Elegy had not been written by Shakespeare, but was much more likely to be from the pen of John Ford.…”

Section: Foster and The Elegymentioning

confidence: 99%

Authorship Attribution

Juola

2007

FNT in Information Retrieval

468

232

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Authorship attribution, the science of inferring characteristics of the author from the characteristics of documents written by that author, is a problem with a long history and a wide range of application. Recent work in "non-traditional" authorship attribution demonstrates the practicality of automatically analyzing documents based on authorial style, but the state of the art is confusing. Analyses are difficult to apply, little is known about type or rate of errors, and few "best practices" are available. In part because of this confusion, the field has perhaps had less uptake and general acceptance than is its due.This review surveys the history and present state of the discipline, presenting some comparative results when available. It shows, first, that the discipline is quite successful, even in difficult cases involving small documents in unfamiliar and less studied languages; it further analyzes the types of analysis and features used and tries to determine characteristics of well-performing systems, finally formulating these in a set of recommendations for best practices.

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Section: Foster and The Elegymentioning

confidence: 99%

Authorship Attribution

Juola

2007

FNT in Information Retrieval

468

232

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“…An interesting consequence of this observation is the notion that some writers are better defined by words they under-utilise, rather than those which they prefer. This has been discussed but not widely supported by in-depth analysis [12], [22], and is one of the motivations of the present work. This variation in frequency may result from the conscious or subconscious censoring of particular words when authors choose formulations for their writing, or rather may be an implicit indicator of a preference for constructions or stances which reduce the need for these words.…”

Section: Introductionmentioning

confidence: 88%

Language Individuation and Marker Words: Shakespeare and His Maxwell's Demon

Marsden

Budden²,

Craig³

et al. 2013

PLoS ONE

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BackgroundWithin the structural and grammatical bounds of a common language, all authors develop their own distinctive writing styles. Whether the relative occurrence of common words can be measured to produce accurate models of authorship is of particular interest. This work introduces a new score that helps to highlight such variations in word occurrence, and is applied to produce models of authorship of a large group of plays from the Shakespearean era.MethodologyA text corpus containing 55,055 unique words was generated from 168 plays from the Shakespearean era (16th and 17th centuries) of undisputed authorship. A new score, CM1, is introduced to measure variation patterns based on the frequency of occurrence of each word for the authors John Fletcher, Ben Jonson, Thomas Middleton and William Shakespeare, compared to the rest of the authors in the study (which provides a reference of relative word usage at that time). A total of 50 WEKA methods were applied for Fletcher, Jonson and Middleton, to identify those which were able to produce models yielding over 90% classification accuracy. This ensemble of WEKA methods was then applied to model Shakespearean authorship across all 168 plays, yielding a Matthews' correlation coefficient (MCC) performance of over 90%. Furthermore, the best model yielded an MCC of 99%.ConclusionsOur results suggest that different authors, while adhering to the structural and grammatical bounds of a common language, develop measurably distinct styles by the tendency to over-utilise or avoid particular common words and phrasings. Considering language and the potential of words as an abstract chaotic system with a high entropy, similarities can be drawn to the Maxwell's Demon thought experiment; authors subconsciously favour or filter certain words, modifying the probability profile in ways that could reflect their individuality and style.

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“…As with the application of the model of Shakespeare's vocabulary (15,37), others have used statistical analysis to identify the author of 12 of the anonymously written Federalist Papers, which both Alexander Hamilton and James Madison claimed to have written, and other apocryphal writings (12,16,17,27,(42)(43)(44). The basic premise of these analyses is that a piece of writing is a representative sample of an author's vocabulary and writing style.…”

Section: Is Darwin Different From Twain?mentioning

confidence: 99%

The Last Word: Books as a Statistical Metaphor for Microbial Communities

Schloss

Handelsman

2007

Annu. Rev. Microbiol.

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Microbial communities contain unparalleled complexity, making them difficult to describe and compare. Characterizing this complexity will contribute to understanding the ecological processes that drive microbe-host interactions, bioremediation, and biogeochemistry. Moreover, an estimate of species richness will provide an indication of the completeness of a community profile. Such estimates are difficult, however, because community structure rarely fits a well-defined distribution. We present a model based on the word usage in books to illustrate the power of statistical tools in describing microbial communities and suggesting biological hypotheses. The model also generates data to test these methods when there are insufficient data in the literature. For example, by simulating the word distribution in books, we can predict the number of words that must be read to estimate the size of the vocabulary used to write the book. Combined with other models that have been used to make inaccessible problems tractable, our book model offers a unique approach to the complex problem of describing microbial diversity.

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And then there were none: Winnowing the Shakespeare claimants

Abstract: The Shakespeare

Cited by 81 publications

References 13 publications

Authorship Attribution

Authorship Attribution

Language Individuation and Marker Words: Shakespeare and His Maxwell's Demon

The Last Word: Books as a Statistical Metaphor for Microbial Communities

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