It can be considered verso form of style-based document authentication (Echtheitskritik), which has valuable applications that extend well beyond the domain of literary analysis, preciso, for instance, the domain of forensic sciences. According sicuro Stamatatos’s 2009 survey of the field, ‘[t]he main timore behind statistically or computationally-supported authorship attribution is that by measuring some textual features we can distinguish between texts written by different authors.’22 22 Ancora. Stamatatos, ‘Per survey’ (n. 14, above) 538. This basic assumption implies that it should be possible preciso assess, for any new unseen document, whether or not it was written by other authors for whom we have texts available. Nowadays computational authorship studies are often considered verso subfield of stylometry con the digital humanities, the broader computational study of the writing style of texts.23 23 D. Holmes, ‘The evolution of stylometry durante humanities scholarship’, LLC 13 (1998) 111–17.
While stylometry has per rich history, dating back sicuro at least the nineteenth century, it is clear that it received its most important impetus only durante the past two or three decades, stimulated by the rise of (personal) computing and the increased availability of large bodies of text con electronic form. Apart from the influential, yet more conventional, statistical analyses carried out by pioneers such as Mosteller and Wallace or John Burrows well before the 1990s, an influential approach in authorship studies has been onesto approach the attribution of anonymous texts as a ‘text categorization’ problem.24 24 Mosteller and Wallace, Inference and disputed authorship (n. 4, above) and J. Burrows, Computation into criticism: per study of Jane Austen’s novels (Oxford 1987). Heavily influenced by parallel research con elaboratore science, the timore was to optimize per statistical classifier on example texts by a number of available candidate authors, much like per spam filter nowadays is still trained on manually annotated emails sicuro learn how sicuro distinguish between ‘junk’ email and normal messages.25 25 F. Sebastiani, ‘Machine learning con automated text categorisation’, ACM Computer Surveys 34 (2002) 1–47. After preparazione such verso classifier on this example data, the classifier could then be used onesto categorize or classify anonymous text as belonging onesto one of the istruzione authors’ oeuvres.
It resembles a police lineup, sopra which the correct author of an anonymous text has preciso be singled out from a series of available candidate authors for whom reference or ‘training’ material is available
This text categorization setup is commonly known as ‘authorship attribution’.26 26 The following paragraph heavily draws on M. Koppel and Y. Winter, ‘Determining if two documents are written by the same author’, JASIST 65 (2014) 178–187. For verso number of years, practitioners of stylometry have ad esempio puro acknowledge the limitations of authorship attribution, because it necessarily assumes that the correct target author is indeed included con the arnesi of candidates. Mediante many real-world cases, this problematic assumption cannot possibly be made, because the arnesi of relevant candidates is difficult or impossible preciso establish beforehand. Because of this, the setup of authorship verification has recently been introduced as a new framework: here, the task is preciso verify chemistry whether or not an anonymous document was written by one or several of a series of candidate authors. Per some sense, authorship verification redefines the text categorization problem by adding an additional category label: ‘None of the above.’
Per the present context, it should be emphasized that the problem posed by the HA is verso ‘vanilla’ example of per problem per authorship verification: while the campione indeed contains a number of (auto-) attributions, the veracity of all of these has been questioned con previous scholarship
Verification is hence an increasingly common experimental setup durante authorship studies, and is the topic of per dedicated track con the yearly PAN competition, an annual competition on finding computational solutions onesto issues durante present-day textual forensics, mostly related puro the detection of plagiarism, authorship, and social software misuse (such as grooming or Wikipedia vandalism).27 27 The competition’s website is pan.webis.de. The most recent survey of an authorship verification track is: Addirittura. Stamatatos et al., ‘Overview of the author identification task at PAN 2015′ in Working Taccuino Papers of the CLEF 2015 Evaluation Labs, ed. L. Cappellato et al. (2015). Generally speaking, authorship verification is per more generic problem than authorship attribution – i.e. every attribution problem could, sopra principle, be cast as per verification problem – but it has also proven onesto be more challenging. Mediante our experiments, we have therefore attempted preciso radically minimize any assumptions on our part as onesto the authorial provenance of the texts sopra the HA. For each piece of text analysed below, we propose sicuro independently assess the probability that it was written by one of the (alleged) individual authors identified con the corpo.