The Early Modern Era is at the forefront of a widespread enthusiasm for Latin works: texts from classical antiquity are given new life, widely re-printed, studied and even repeatedly staged, in the case of dramas, throughout Europe. Also, new Latin comedies are again written in quantities never seen before (at least 10,000 works published 1500 to 1800 are known). The authors themselves, within the game of literary imitation (the process of imitatio), start to mimic the style of ancient authors, and Terence's dramas in particular were considered the prime sources of reuse for many decades. Via a case study ”the reception of Terence's Eunuchus in Early Modern literature”, we take a deep dive into the mechanisms of literary imitation. Our analysis is based on four comedy corpora in Latin, Italian, French and English, spanning roughly 3 centuries (1400-1700). To assess the problem of language shi昀琀 and multi-language intercorpora analysis, we base our experiments on translations of thEeunuchus, one for each sub-corpus. Through the use of tools drawn from the 昀椀eld of Stylometry, we address the topic of text reuse and textual similarities between Terence's text and Early-Modern corpora to get a better grasp on the internal 氀昀uctuations of the imitation game between Early Modern and Classical authors.
In the last few decades, Stylometry has been used to track authorial signals and stylistic
similarities between authors with great e昀ectiveness ([
The remainder of this paper is structured as follows. Sectio2ngives a brief overview of the related research. Section3 presents the data and the process of structuring our corpus. Section 4 sketches our experimental setup. Section5 gives a detailed analysis of the results and a discussion on a literary history level. Sectio6nconcludes the paper with an overview on future work.
Our processing line builds on very stable and already frequented ground. Stylometric tools
of di昀erent origin and applications have been gaining popularity since the work of Burrows,
Holmes, and Craig in the early ’90s4[
A number of stylometric studies has been devoted to tasks such as authorship attribution and
detection (cf. [
More akin to our topic is that of style variation analysis and stylistic similarities (cf3.2[]
for an overview): [
The core statistics and algorithms we use in this paper are Burrows’ Delta and Sequential
Stylometric Analysis (SSA), being already known for their e昀케ciency in the 昀椀eld of stylistic
analysis. For an in-depth overview of Burrows’ Delta and, in general, Delta analysis for textual
similarity tasks, see [
We collected a corpus of 85 comedy pieces from the DraCor Proje1cdtatabase (English, French, and Italian) and the Translatin repository (Latin2). The original classical Latin text of theEunuchus is taken from the LASLA corpus3.
The corpus statistics are shown in Table1
As is visible from the time spans, the covered period varies, but it 昀椀ts the general boundaries of the Modern Era (roughly: early 15th - late 18th centuries). A practical reason for this diversi昀椀ed selection is due to the selection o昀ered by DraCor and in literary history: the two most famous Italian comedy writers of the Modern Era, Ariosto and Goldoni, are set apart by 2 centuries, while the vast majority of the most important drama writers of France’s Modern Era (Molière, Racine, Corneille) are found in the middle of the 17th century. For English, DraCor only possessed the complete Shakespeare corpus, from which we selected 15 comedies. Finally, we added a random selection of Neo-Latin works from a wide variety of authors, nationalities and years of production, drawn from our project repository, automatically cleaned and manually checked from a previous OCR proces4s.
For this whole experiment, we wanted the translations to be in the background and cause as little noise as possible to keep the focus on the original work by Terence. The translations were thus gathered according to the following criteria: 1. As close as possible (time-wise) to the period span of its related sub-corpus; 2. Freely available and downloadable; 3. A philological translation, as close as possible (style-wise) to the original from Terence. The 昀椀rst and third criteria helped make up for the linguistic and stylistic divide. If contemporary translations had been taken into consideration, the experiment would have been invalidated from the start, the language of those translations being too distant from their relative Modern Era counterparts. The third point poses another subtle but important aspect: the translation must be ”philological”, thus as close as possible to the unadulterated original. This rules 1https://dracor.org/Last visited: 29 August 2022 2https://www.translatin.nl 3https://www.lasla.uliege.be/cms/c_8508894/fr/laslLaast visited: 29 August 2022 4The whole corpus, together with the parameter speci昀椀cations for the Stylo code, can be found in our GitHub repository:https://github.com/AndrewPeverells/The-Imitation-Game out, for example, an ”artistic” translation of Terence’s works by Nicolò Machiavelli, supposedly of very little rigorousness as far as adherence to the original was concern5eSdp.elling variants were uni昀椀ed with a layer of pre-processing from the CLTK/NLTK pipeline6.
The translations meeting all criteria at the time of our experiment are: • FRENCH - H. Clouard, 1937 • ITALIAN - L. Perelli, 1869 • ENGLISH - G. Colman, 1768 The Italian and French translations, while rather late, are still su昀케ciently suitable, as French altered very little from the end of the 17th century, while Perelli’s translation of 1869 still falls roughly under the same pre-uni昀椀ed contemporary Italian.
In this section, we given an overview of our experiments. We start with a preliminary exploration of our dataset using a clustering algorithm to identify general groupings within our corpus. Then we analyse our texts in their sequential development, with the aim of identifying overlaps and distances between our modern drama sub-corpus and their relative translations of the Eunuchus, to accurately identify modern authorial 昀椀ngerprints and takeovers against Terence’s piece, throughout the succession of acts and scenes of the original.
As a pre-processing step, we divided our corpus into two distinct sets: the primary set (test set), composed of the 81 modern texts, subdivided into four language corpora; and the secondary set (training or reference set), composed of the four versions of thEeunuchus, again subdivided per language. The training or reference set is the relevant Eunuchus translation, or the ”known” text, against which our test is to be conducted to identify authorial signals and similarities. For the 昀椀rst experiment, the clustering analysis, this subdivision is irrelevant, since we need to distribute all the texts together into di昀erent clusters, and we need the Eunuchus to be evident in our clusters. The general parameters that were kept for both sets of experiments are the following, based on our previous experiment26[]: • To overcome issues related with an arbitrary selection of MFWs (Most Frequent Words), we ran several trials on every setting from 100 to 1500 MFW7.s.We found 300 MFWs to be the most suitable for our experiment, as the texts are not too long (they rarely exceed 15,000 words) and we noticed that, over this number, the clustering began to gradually merge every branch together. This was already observed in our recent experimen26t][ and is con昀椀rmed by [ 18]. This somewhat low parameter of 300 MFWs makes the set of words on which our analysis is conducted almost entirely comprised of function words 5Cf. the article on Terence from the ItalianEnciclopedia Machiavelliana 6NLTK, and CLTK Last visited: 22 August 2022 7A practice already well-established in the 昀椀eld of Stylometry:11[]
and a handful of high-frequency semantically meaningful words, mainly related to the dramatic language (e.g. for English ”lord, father, lady, pray, brother, true”, or for Latin ”quaeso, mehercle, dico, amor, senex”); • Contractions were not removed; • No 昀椀ltering of the function words was introduced, thus keeping the texts as they appear originally. Interjections and personal pronouns especially were not removed on purpose, as they are a vital part of the dramatic language; • Maximum culling was set to 20%, meaning that a word needs to appear in at least 20% of the text, to eliminate some background noise.
The 昀椀rst experiment set is a cluster analysis produced via a Delta algorithm. The selected delta
measure is Burrows 3[], which is widely used for stylistic analysis as a reliable similarity
measure between two candidates (cf. [33]). Burrows’ Delta is also con昀椀rmed to be better suited for
shorter-vector corpora2[
A simpli昀椀ed formula of Burrows’s Delta, which enriches Burrows’s original by taking into account standard deviations between word frequencies, is given in Equation1: Δ(ý )(ÿ, ÿ′) = ∑ 1 | (ÿ) − (ÿ′)| =1 (1)
Wheren is the set of MFWs, the subscript B indicates the relation to Burrows’s original Delta, | (ÿ) − (ÿ′ )| the computed normalised di昀erence between the frequency of a word in a text D, and its standard deviation.
Using the 300 MFWs max-culling 20% parameters we introduced in Subsectio4n.1, we calculated and produced clusters for each sub-corpus as shown in Figur1e-s4.
We then produced a slightly modi昀椀ed version of this delta analysis by implementing the Rolling Delta procedure initially proposed b8y] [and implemented in the R Stylo package8. This methodology, instead of inspecting the whole corpus as one batch and calculating the 8https://www.rdocumentation.org/packages/stylo/versions/0.7.4/topics/rolling.delta
Delta distance from the sum frequency of two texts, subdivides every text into equal-sized windows, following the evolution (in words) of the reference set text, and then compares each of these windows for every text in the corpus. This is particularly useful to track stylistic shi昀琀s along the evolution of a text’s length.
First, each text is divided into samples, or ”windows”. Then the centroid (C) is computed for the mean relative frequency of then most frequent words of each window. The centroid C then consists of a one-dimensional vector composed of 3 elements: the mean frequencies ( ) computed against the relative frequencies ( ) of the window samples, and enriched with the standard deviation for each of then’s (most frequent words) relative frequencies for each window sample. Finally, the standard Delta is computed for each windowW() and its relative reference C (centroid), calculated as above as shown by Equatio2n.
Δ(þ, ) = ∑ =1 (þ) 1 | (þ) − ( )| (2)
A昀琀er having ”rolled” through each window, the result is the plotted Delta in an x,y axes space,
where the x-axis corresponds to the evolution (in words) of the texts, and their relative window
samples, against the reference set (theEunuchus) and the y-axis to the Delta distance. Thus,
the closer to the x-axis, the higher the similarity; the further away from the x-axis (= higher
Delta), the lower the similarity. This methodology was originally developed for authorship
attribution and detection [
The second experiment focuses on the use of Sequential Stylometric Analysis10[], which takes the previous method of calculating distances between texts in regards to their evolution in words (hence ”sequential”), and combines it with the application of a machine-learning algorithm, such as -Nearest Neighbor, Support Vector Machine, Naive Bayes, and Nearest Shrunken Centroid (NSC).
We chose the NSC algorithm as it is already widely and successfully used in the 昀椀eld of
Stylometry [
The NSC algorithm is a form of feature selection and evaluation: it performs the evaluation
of a sample, extracting classes of features and shi昀琀ing them towards the more central ones
(centroids, interpreted as the geometric centre of a data distribution set and calculated as the
mean average value of each feature) and removing the more distant ones as noise (shrinking)
until only a few classes of features have an actual impact on the classi昀椀cation. The algorithm
calculates the centroid for each class group in the dataset, thus assigning a model label to each
class group based on its relative centroid; a昀琀er the shrinkage, the remaining centroids are the
ones composing the general model on which the Delta distance is calculated. For an in-depth
mathematical analysis of the NSC classi昀椀er applied to stylometry, see2[
The NSC algorithm, built in the R package Stylo1[
In this section, we present and discuss the results from our experiments and combine them together to have a general framework of the authors’ preferences towards a model, thus gaining more insights on the general process ofimitatio. First we discuss each sub-language corpus separately, followed by a summary of the general tendencies that stand out from our analysis.
From the clustering part of the experiment4)( we note a clear closeness between earlier works
(16th century) and theEunuchus, despite its translation being closer to Goldoni. This similarity
is especially evident for the works of MachiavellMi(andragola and Clizia) and Ariosto: they
were both notorious connoisseurs of Terence, the former even producing one of the 昀椀rst Italian
translations of some of its works, while the latter was responsible for numerous re-enactments
of Terence’s comedies (especially theEunuchus), both in Latin and Italian, when he was in
Ferrara at the Este cour9t.) We can therefore assume that Terence’s style was deeply rooted in
their own, while theEunuchus had little to no in昀氀uence in the later stages of Italian comedy
production. The clear-cut distance between two texts closely related to Terence and the Este
court, that was a the forefront of the revitalisation of Latin drama (especially Terence) at the end
of the 15th century.10 Both Comedia di Timon Greco by Galeotto del Carretto (who dedicated his
work to Beatrice d’Este) and theComedia di Danae by Baldassarre Taccone (who was patronised
9For an overview on Ariosto’s life and production and the literary role of the Este court in Italian late Renaissance,
see [
From the SSA experiment (Figure8), the closest relative to theEunuchus appears to be I
Suppositi, in the largest part, being closest to Terence’s play at the start and at the end. Both
works are a switching doubles comedy, and in both many of the characters go undercover.
We interpret this in the lights of the fact that the start and ending of a switching doubles
comedy are especially important in the setting-up of the disguise and the 昀椀nal resolution of
the misunderstanding at the very heart of the comedy, while the actual central plot of scheming
and deception is le昀琀 to the invention of the author (being the central part, up until the very
end of the piece, the most interpolated section). The turning point between the end of act IV
and the start of act V, right before the 昀椀nal resolution, is instead taken by Ariosto’sCassaria, a
plautine-inspired comedy.
5.2. Latin
The clustering algorithm automatically drew two very distinct clusters (see
Fig2u)r,eseparating the 16th century works from the 17th century ones, and the Eunuchus is the clear dominant
model in the 昀椀rst 16th century cluster. This is con昀椀rmed by literary scholars in for example26[]
and [
One exception is in the 1615 text by Jacob Bidermann. A possible explanation is that Bidermann is the only Jesuit in our 17th century cluster: our previous pape2r6(][) con昀椀rmed the general tendency of 16th century catholic authors, such as Macropedius, Crocus, Diether,
Simonides and Schonaeus, to heavily favour Terence as a literary model (before switching to Seneca).
From the sequential analysis (Figur9e), the closest overlapping relatives to the Eunuchus are
Crocus and Macropedius, which take more than half of the work’s body. This is an already
wellestabilished parallel, in line with the start of the 16th century’s widespread passion for terentian
drama. As a general note, the Latin sub-corpus appears to be the one with the heaviest and most
varied in昀氀uences from the Eunuchus, with the least branching in the clustering and the most
numerous switches in author similarity in the SSA analysis. This con昀椀rms the heavy usage
of textual instances from Neo-Latin authors towards their models, rather than an underlying
echo of in昀氀uence: Neo-Latin authors read, copied, transcribed, imitated, staged, and taught
ancient authors on a daily basis [
From the cluster analysis (Figur3e) we can observe the almost complete preponderance of Molière, the very minor presence of Corneille and complete absence of Racine, Fontaine and other minor authors. Furthermore, the complete distance of FontaineE’sunuque from the original model on which it is based stands out. Even though Fontaine’s work is based upon Terence’s, his style is apparently very di昀erent (a consideration perfectly in line with the chosen methodology, stylometry, which is in most cases considered independent of content and semantics).
From the sequential analysis (Figure7), the absolute winners of thisimitation game with Terence areLes Fourberies de Scapin by Molière andLes Fausses Vérités by Antoine d’Ouville. Both are comedies of love intrigue and infatuation of a man for a young girl that are stylistically in昀氀uenced by the prior Italian comedy, which is in turn heavily Terentian. As for Ariosto, one particularl workL(es Fausses Vérités) takes the parts of I Suppositi, gathering heavy in昀氀uences from the very start and end of theEunuchus; for the remaining part of the playL,es Fausses Vérités and Les Fourberies de Scapin battle themselves for predominance, continuously switching primacy for the in昀氀uence within the Eunuchus. Again, the most interpolated part seems to be the second half of Terence’s work, with act IV displaying the heaviest in昀氀uences.
The English sub-corpus presents a di昀erent situation, because all the results are negative. From the clustering experiment (Figure1) we note a clear-cut distance between theEunuchus and the Shakespearean comedy corpus. This poses the following questions: could this distance be due to the translation not being contemporary to Shakespeare? although it is the oldest and the closest to the test corpus out of all the languages inquired? This also poses a question about style and language that deserves further investigation: is it the diachronical variation of language or is it Shakespeare’s style that sets them apart? Is this issue due to the language of Colman’s translation or is it entirely to be attributed to Shakespeare’s notoriously idiosyncratic and peculiar style, so that even a contemporary translation would not be su昀케cient to track similarities between his corpus and Terence’s works? This renders necessary a digitisation of Webbe’s translation (1638), which is, to our knowledge, the closest to Shakespeare’s times done by a ”professional”11 and, at the time of our experiment, not freely available. 11In our experiment, at 昀椀rst, we used William Heming’s 1602 translation, but the results were even worse: the delta distance between Heming and Shakespeare’s comedies more than tripled relative to our current test Colman.
Finally, From the sequential analysis (Figur6e), the closest candidate is The Comedy of Errors, but it is a result that cannot be trusted: the overlapping line is almost negligibly thin, thus the score of Delta distance is comparably quite high.
From our experiments, and by looking at the results shown by the Rolling Delta visualisation on the primary sub-case study of Neo-Latin (Figure5), we can draw some conclusions about the in昀氀uence of Terence’s Eunuchus on the Modern Era drama production, that give us some insights on the underlying process of imitation towards ancient authors.
• Act IV seems to be the most interpolated and reused, as shown from the high coincidence of overlaps in the SSA. This indicates a preference, by modern authors, for taking inspiration from a speci昀椀c topos in classical theatre writing, as the fourth act always corresponds to an escalation in the web of intrigues and a turning point in the general plot before the 昀椀nal settlement: we can then assume that a particularly animated style of narration is at work and modern authors tend to be close to it; • Act I and V, the opening and closing of the comedy, are always taken by a speci昀椀c work, from the modern perspective. Act I and V are inextricably tied to the speci昀椀c play’s plot and are usually made up almost exclusively of fast-paced spoken dialogues (monologues and reported speech, typically from serfs, taking up the middle of the play): the new characters are presented (Act I) and their misadventures are resolved in a turning of events (Act V), so it makes sense that only works with a similar story could tie to their speci昀椀c style, mimicking in particular the new characters’ exchange of gags and blows.
From the Rolling Delta analysis, applied to the sub-case study of Neo-Latin against the originalEunuchus, we can note some clear patterns: • The imitation game follows a rough ups-and-downs style, with two clear areas of low and high delta, respectively: the end of act II, act III scene 5, the end of act IV, and the ending of the play; • The 昀椀rst low delta (= high similarity and overlap with the original) corresponds to the end of the initial story set-up and character presentation, where usually the characters starts to get into the thick of the machinations, con昀椀rming again that the usefulness of the original play stops when the plot overcomes the possible usage of styloms (that is, when modern plays’ stories become too distant from theEunuchus to justify the re-use of style); • The 昀椀rst high delta (= low similarity) perfectly overlaps with the most famous of Terence’s scenes: the rape scene of act III scene 5. Although in reported speech, this scene goes into details of the rape, and it is surprising that the Neo-Latin authors do not make use of the seduction and emotional violence styloms of Terence’s scene; This is probably due to Heming’s very loose translation: he was a playwright and a poet, not a translation expert, grammarian and language teacher as Webbe was. • The second low delta corresponds to the end of act IV, and it ties in to the aforementioned turning point in the comedy’s structure; • The second, and last, high variation section coincides with the near-end of the play, but it shows a very interesting pattern: on the one hand, plays that originally turned out to be very close to theEunuchus from the other parts of our experiment, at that point plunge even further down, reaching a new low delta and con昀椀rming their similarity in the important ending section of the play; on the other hand, works that originally turned out to be distant, sport an opposite fashion, going ever upward in their delta distance and reaching the second highest point of dissimilarity.
In this paper, we described a case study for the application of computational methods on the issue of assessing the process ofimitatio between authors from the Early Modern Period and classical models. We started by gathering a corpus, consisting of one of Terence’s works as a case study, the Eunuchus, its translations in another three languages (English, Italian and French), and four sub-corpora of drama from the Early Modern Period, in the four respective languages, that served as the proper test set. We then explained the methodology we employed for our experiment and the two di昀erent and complementary analysis it enabled us to perform: Cluster Analysis through Delta measures, and Sequential Stylometric Analysis. We then proceeded to the in-depth analysis of the results for each of the 4 languages, evaluating the peculiarities of each sub-corpus and the broader patterns that stood out. Finally, we drew some general conclusions on the process of modern authors’ imitation of classics within theatrical writing.
By this, we achieved our initial, more general, aim of describing the methodology for multilanguage literary studies that can be used for other case studies beyond Terence, for example: 20th century authors reusing Renaissance authors). Furthermore, provided the correct parameters (such as the act-scene subdivision for drama or the verse-stanza structure for poetry), our methodology can be used not only for inquiring drama, but any other genre. The combinations coming out of the possibilities given by such methodology are copious, as many other studies showed in the past (Section 2).
However, the chosen methodology has limitations. Stylometry only tackles issues of style in a purely ”formal” way, that is by only taking most frequent wor1d2s,it is by no means a methodology for semantic analysis, and it only provides tools for a distant reading environment. Furthermore, it showed its internal limitations in the analysis of the English sub-corpus (Subsection 5.4), when it yielded poor results both in the Cluster Analysis and the SSA. Conversely, Stylometry can o昀琀en catch hidden patterns especially thanks to its core features (distant reading environment and function words analysis): Stylometry has proven successful and useful when analysing literary corpora with the aim of building networks of common and dissimilar features, o昀琀en handling quite large sets of these features at the same time. To us therefore, 12MFWs are o昀琀en going to be function words in a traditional literary work, but for other cases this depends on the type of input text. There is also no consensus on the exact de昀椀nition of function words. Stylometry is not to be taken on its own, but to be combined with other methodologies that can complement its structural 昀氀aws, and in turn be enriched by Stylometry’s unique take.
It is with these caveats in mind that we plan to expand our methodology with implementations from other methods, primarily semantic analysis. Internally, one critical step to cement our 昀椀ndings would be to access to proper contemporary (to the Modern Era) translations of the Eunuchus, to get rid of every possible imprecision due to the distance between the author’s language and the translation’s very own, while one entire sub-project could devoted to comparing di昀erent translations and how they perform. Furthermore, we deem a mandatory step to expand our reference corpus of classical drama writers to the point of including every Latin play and replicating our methodology on each one of them. Finally, enriching our corpus with new data from the copious drama production of the Modern Era would be a natural next step. This would bring our the ultimate goal of accounting for the complex issueiomfitatio within Modern Era drama writing closer.
This research is conducted within the framework of theTransLatin projectfunded by the Dutch Research Council (NWO).
H. Þorgeirsson. “”How similar are Heimskringla and Egils saga? An application of Burrows’ delta to Icelandic texts””. In:European Journal of Scandinavian Studies 48, Issue 1 (2018).