Dating G reek Papyri with Text Regression (2023)

Abstract: Dating Greek papyri accurately is crucial not only to edit their texts but also to understand numerous other aspects of ancient writing, document and book production and circulation, as well as various other aspects of administration, everyday life and intellectual history of antiquity. Although a substantial number of Greek papyri documents bear a date or other conclusive data as to their chronological placement, an even larger number can only be dated tentatively or in approximation, due to the lack of decisive evidence. By creating a dataset of 389 transcriptions of documentary Greek papyri, we train 389 regression models and we predict a date for the papyri with an average MAE of 54 years and an MSE of 1.17, outperforming image classifiers and other baselines. Last, we release date estimations for 159 manuscripts, for which only the upper limit is known.

## introduction ancient textual artefacts are arguably the richest source of information on the ancient world. in the graeco-roman world and particularly in its greekspeaking part, the most extensive coeval texts come from inscriptions and papyri. the latter is a collective term used for all ancient manuscripts, regardless of their writing material which, apart from papyrus, may be parchment, pottery, wood, and others. to correctly evaluate and make good use of these texts, we need to determine their date, provenance and historical context of their production and use. as far as dating is concerned, the value of the relevant evidence provided by the artefacts themselves varies considerably, ranging from a direct date in the text (following, of course, the calendar and dating system of the respective historical period) to no evidence at all. in between, there are texts containing references to known historical figures and events of a certain period, papyri which have been found next to other objects that can be dated, or other indirect evidence. the presence or absence of a date depends on the type of text preserved on the papyrus and its use through time, as well as on its state of conservation. just like in modern times, it is much more likely to include a date in an official letter than in a page torn from a novel book. at the same time, it is more probable to find a date in a fully surviving letter than in a damaged one missing, for instance, the upper part of the first page. greek papyri, which mostly survive in fragments, are divided into two broad categories: books (literary and sub-literary papyri) and documents of all kinds (documentary papyri). the former ones never carry a date, whereas the latter often do, albeit not always unambiguously convertible by modern scholars. most importantly for our study, literary papyri contain copies of works authored many years (often centuries) before the production of the actual manuscripts. on the other hand, documentary texts were usually written down as they were composed or shortly after that, making the content of their texts contemporary to their writing style or script. therefore, any temporal indication in the text is also dating evidence regarding the production of the document. even when there is no direct date in the text (e.g. figure 1 ), documentary papyri can be dated securely sometimes within a short time-frame, because they may refer to known historical events or concern people known through other sources to have lived at a particular time. when neither direct or indirect dating is possible, papyrologists resort to palaeography, the study of the script. in palaeography, particular writing styles are associated with certain chronological periods. therefore, similar writing styles point to similar dates @xcite . securely dated specimens are used as a guide to chronologically place the undated ones. growing criticism on the subjectivity of palaeographical dating @xcite @xcite highlights the need for more reliable methods. recent efforts for computational dating of historical manuscripts are based on the script rather than the text and, although they consider various languages, they disregard @xcite . in this study we focus on computational dating of greek documentary papyri based on their transcriptions, contributing in the following three ways: this approach does not apply to literary papyri and our research involves solely documents. apart from their texts being contemporary with the actual manuscripts (by dating the text, we date the papyrus), nonliterary papyri also include vastly more numerous objectively dated specimens than literary ones. specific dates on our training set also allow for more accurate (narrower date-spans) predictions by our models. ## related work dating historical documents with computational means has been studied for many languages @xcite @xcite @xcite . however, very limited work has been done for greek and no published work at all has focused on greek papyri. the only work to our knowledge is ithaca, a transformer trained on ancient greek inscriptions performing text restoration, geographical attribution, and dating @xcite . ithaca has achieved an error of 0.29 centuries in dating epigraphs. this result is by far better than an onomastic baseline using the known distribution of greek personal names to infer the date, which scored 1.44. inscriptions differ from papyri in many aspects (such as the genre, the length, and their geographical distribution), but in principle, this system is applicable to our data and was therefore used as a baseline. below, given the absence of dating studies for greek, we summarise work for other languages. the studied languages are latin @xcite @xcite , hebrew @xcite , dutch @xcite @xcite , arabic @xcite , swedish @xcite , french @xcite and english @xcite . a collection of 595 dead sea scrolls, in aramaic script, was the dataset with the oldest manuscripts, dated from 250 to 135 bce, and the only one so far concerning texts written on papyri @xcite . the rest of the datasets comprised more data, ranging from less than five @xcite to more than ten thousand manuscripts @xcite or more @xcite , while the one with the most recent manuscripts comprises historical english-language documents @xcite , printed between the 15th and 19th ce. the employed methods usually were standard machine learning methods, such as knn @xcite , decision trees @xcite , random forests @xcite and support vector machines @xcite @xcite . textural features, such as gabor filters, uniform local binary patterns and histogram of local binary patterns are extracted and then fed to the classifiers @xcite . the writing style evolution, however, has also been used as an intermediate step @xcite . in this case, the periods are first aligned with specific writing styles. then, any new manuscript is dated based on the detected style. pre-trained convolutional neural networks have been used to extract features, which are passed to a classifier or regressor @xcite , or used in combination with text features extracted with optical character recognition methods @xcite . transfer learning has been reported to lead to human performance @xcite . this was deemed to be the most promising direction for the present study on greek manuscripts, and was, hence, employed. ## data our dataset, which we release publicly, foot_0 comprises the transcriptions of 389 manuscripts, dated from the 3rd century bce to the 7th century ce, originating from greco-roman egypt (with a few exceptions from the near-east). ## methodology to estimate the date of production of manuscripts, we opted for text regression, taking advantage of the continuous target objective. statistical validity was established with 5-fold monte carlo crossvalidation. the best regression method was used to form a committee of models, which were applied on unseen data in order to analyse the predictions. ## empirical analysis this section presents our experimental results using regression on textual features to date greek manuscripts. first, we present preliminary experiments and then we analyse the experimental findings from our regression analysis. ## use cases we applied our 389 regressors, produced upon leave-one-out cross-validation, to three use cases, which present different types of dating challenges. ## discussion the computational, quantitative method suggested in this work is intended to complement human expertise. its main contribution lies in providing an additional dating criterion for ancient greek documents, in addition to the ones usually employed by papyrologists (palaeography, onomastics, prosopography, toponymy, archaeological evidence, etc.). it can predict a date for those papyri that do not include one, narrow down the possible time-span of doubtful dating, or contribute to deciding on one particular date when several alternatives seem possible. despite the fact that limitations exist (discussed in §7.3), compared to traditional approaches the models trained in this study are expected to reduce biases. their value is not limited to predicting dates for individual manuscripts, but they can be applied to any attribute of a group of papyri, e.g. the place of provenance or the text's type. at the same time, easily accessible open-source metadata exist for most published papyri ( §3.1). ## conclusion greek documentary papyri of (mostly) egyptian provenance, dated and balanced in terms of chronological quarter-century distribution. we trained extremely randomised trees on top of character n-gram-based features, reaching a mean absolute error of 54 years and 60% in century-level classification accuracy. we then formed a committee of regressors, which we applied to three use cases: a land lease, a private letter, and a geographical outsider (not from egypt). to assist future research, our committee dated 159 manuscripts, for which only the upper limit is known. future endeavours for this research extend far beyond the dating of individual manuscripts. it can produce valuable data for the study of the greek language and its evolution through a millennium, help identify and trace linguistic habits and trends, as well as the history of document production, circulation, and use (e.g. which period produces what kind of texts, which administration relied on what type of documents, etc.). it can also produce further data and resources towards the typology of ancient greek documents, completing with computational methods the work already underway and well-advanced of the grammateus project. last, it can in the future fruitfully be combined with computational paleography to analyse the script and content of a given text.