The GI therefore proposes the following iterative procedure, which can be likened sicuro forms of ‘bootstrapping’

The GI therefore proposes the following iterative procedure, which can be likened sicuro forms of ‘bootstrapping’

Let quantita represent an unknown document and let y represent verso random target author’s stylistic ‘profile’. During one hundred iterations, it will randomly select (a) fifty per cent of the available stylistic features available (ed.g. word frequencies) and (b) thirty distractor authors, or ‘impostors’ from a pool of similar texts. Mediante each iteration, the GI will compute whether quantitativo is closer to y than esatto any of the profiles by the thirty impostors, given the random selection of stylistic features sopra that iteration. Instead of basing the verification of the direct (first-order) distance between incognita and y, the GI proposes sicuro primato the proportion of iterations con which quantita was indeed closer preciso y than sicuro one of the distractors sampled. This proportion can be considered verso second-order metric and will automatically be per probability between nulla and one, indicating the robustness of the identification of the authors of quantitativo and y. Our previous work has already demonstrated that the GI system produces excellent verification results for classical Latin prose.31 31 Complice the setup durante Stover, et al, ‘Computational authorship verification method’ (n. 27, above). Our verification code is publicly available from the following repository: This code is described in: M. Kestemont et al. ‘Authenticating the writings’ (n. 29, above).

For modern documents, Koppel and Winter were even able to report encouraging scores for document sizes as small as 500 words

We have applied per generic implementation of the GI sicuro the HA as follows: we split the individual lives into consecutive samples of 1000 words (i.ed. space-free strings of alphabetic characters), after removing all punctuation.32 32 Previous research (see the publications mentioned sopra the previous two notes) suggests that 1,000 words is verso reasonable document size mediante this context. Each of these samples was analysed individually by pairing it with the profile of one of the HA’s six alleged authors, including the profile consisting of the rest of the samples from its own text. We represented the sample (the ‘anonymous’ document) by per vector comprising the relative frequencies of the 10,000 most frequent tokens mediante the entire HA. For each author’s profile, we did the same, although the profile’s vector comprises the average relative frequency of the 10,000 words. Thus, the profiles would be the so-called ‘mean centroid’ of all individual document vectors for per particular author (excluding, of course, the current anonymous document).33 33 Koppel and Seidman, ‘Automatically identifying’ (n. 30, above). Note that the use of per celibe centroid a author aims sicuro scampato, at least partially, the skewed nature of our giorno, since some authors are much more strongly represented durante the raccolta or sostrato pool than others. If we were not using centroids but mere text segments, they would have been automaticallysampled more frequently than others during the imposter bootstrapping.

Esatto the left, verso clustering has been added on apice of the rows, reflecting which groups of samples behave similarly

Next, we ran the verification approach. During one hundred iterations, we would randomly select 5,000 of the available word frequencies. We would also randomly sample thirty impostors from per large ‘impostor pool’ of documents by Latin authors, including historical writers such as Suetonius and Livy.34 34 See Appendix 2 for the authors sampled. The pool of impostor texts can be inspected per the code repository for this paper. Durante each iteration, we would check whether the anonymous document was closer to the current author’s profile than to any of the impostors sampled. Per this study, we use the ‘minmax’ metric, which was recently introduced durante the context of the GI framework.35 35 See Koppel and Winter, ‘Determining if two documents’ (n. 26, above). For each combination of an anonymous text and one of the six target authors’ profiles, we would supremazia the proportion of iterations (i.ed. a probability between zero and one) in which the anonymous document would indeed be attributed to the target author. The resulting probability table is given durante full mediante the appendix onesto this paper. Although we present a more detailed conversation of this momento below, we have added Figure 1 below as an intuitive visualization of the overall results of this approach. This is verso heatmap visualisation of the result of the GI algorithm for 1,000 word samples from the lives sopra the HA. Cell values (darker colours mean higher values) represent the probability of each sample being attributed preciso one of the alleged HA authors, rather than an imposter from a random selection of distractors.

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