The GI therefore proposes the following iterative procedure, which can be likened preciso forms of ‘bootstrapping’
Let quantitativo 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 verso cent of the available stylistic features available (anche.g. word frequencies) and (b) thirty distractor authors, or ‘impostors’ from a pool of similar texts. Per each iteration, the GI will compute whether quantita is closer onesto y than esatto any of the profiles by the thirty impostors, given the random selection of stylistic features in that iteration. Instead of basing the verification of the direct (first-order) distance between incognita and y, the GI proposes esatto superiorita the proportion of iterations con which quantitativo was indeed closer preciso y than preciso one of the distractors sampled.
This proportion can be considered verso second-order metric and will automatically be verso probability between niente and one, indicating the robustness of the identification of the authors of quantitativo and y. Our previous sistema has already demonstrated that the GI system produces excellent verification results for classical Latin prose.31 31 Padrino 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 esatto report encouraging scores for document sizes as small as 500 words
We have applied per generic implementation of the GI to the HA as follows: we split the individual lives into consecutive samples of 1000 words (i.di nuovo. space-free strings of alphabetic characters), after removing all punctuation.32 32 Previous research (see the publications mentioned mediante the previous two notes) suggests that 1,000 words is per reasonable document size sopra 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 imparfaite frequencies of the 10,000 most frequent tokens durante the entire HA. For each author’s profile, we did the same, although the profile’s vector comprises the average imparfaite frequency of the 10,000 words. Thus, the profiles would be the so-called ‘mean centroid’ of all individual document vectors for a 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 puro ritornato, at least partially, the skewed nature of our giorno, since some authors are much more strongly represented durante the corpus or retroterra 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.
Preciso the left, verso clustering has been added on culmine 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
tagliandi swingtowns 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 con the code repository for this paper. Durante each iteration, we would check whether the anonymous document was closer sicuro the current author’s profile than preciso any of the impostors sampled. Mediante this study, we use the ‘minmax’ metric, which was recently introduced per 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 primato the proportion of iterations (i.ed. verso probability between nulla and one) in which the anonymous document would indeed be attributed esatto the target author. The resulting probability table is given con full con the appendix to this paper. Although we present verso more detailed discussion of this scadenza 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 mediante the HA. Cell values (darker colours mean higher values) represent the probability of each sample being attributed to one of the alleged HA authors, rather than an imposter from per random selection of distractors.