Tutorial anonymus translators (en)

Aus Kallimachos
Wechseln zu:Navigation, Suche

Tutorial for identification of anonymous arabic-latin translators

typische Wörter für Dominicus Gundisalvi als Wordcloud

Composition of a text corpus

The aim of research for the project in is the identification of anonymous arabic-latin translations in medieval times by means of philological and computer-aided methods of style analysis. For this purpose, a corpus of electronic latin texts must be constructed. It's advisable to restrict the corpus to a certain arabic author, e.g. Averroes, or to a technical discipline, e.g. philosophy, astronomy/astrology, medicine, mathematics, alchemy/macig/prophecy or religion. However, this is only possible if the corpus is large enough. At Wuerzburg University an Averroes-based corpus (Hasse 2010) and two corpora with philosophical and astronomical/astrological translations of 12 century were formed and employed (Hasse 2016 and Hasse-Büttner in print). Herein, we were able to benefit from a list of philosophical arabic-latin translations already provided by Burnett in 2005, as well as Carmody in 1956 with a list of astronomic-astrologic translations (which are imprecisely and obsolete, though). In other branches of science, such lists have yet to be created.

Translations are available in very different text formats: Some are critically edited, others are only available in earlier printings or only in medieval handwritings. The OCR of modern editions is largely unproblematic. A relieable OCR of early printings, where the computer has to "learn" the officin's characters, is currently a subject of University of Wuerzburg and DFKI Kaiserslautern. At present, it's still advisable to transcribe early printings manually. With hand writings, the manual transcription will be the only viable option for a long time. A preferable textual witness should be chosen, which is especially one who provides a complete and non-revised text (latin authors of early printings are listed at Hasse, Success and Suppression, 2016, S. 317-407).

It's highly recommended to systematically seperate and index scans and the files produced due to further processing. This can be done simply by using seperated subfolders and seperatly managed spreadsheet or by means of a wiki program. This step may seem self-explanatory, but is also overlooked quite easily. The following aspacts should always be distinguished:

  1. the bibliographic mark of origin
  2. the scan
  3. the fully searchable and quotable scan
  4. a text cleaned of all non-textual features (page numbers, critical apparatus etc.)
  5. a normalized orthographic text made for stylometry (e.g. as a simple text file)

Processing the texts for comparative analysis

The citable text (3) isn´t usable for stylometry yet, but can be useful for other scientific tasks. Of course, to be able to compare texts using stylometry, they need to be made comparable beforehand. In the field of medieval editions, punctuation rules and orthography are major obstacles, for the punctuation rules often vary according to the national customs of the editors (german, french, english etc.), while the "signal" of the author ist lost. In turn, the orthography ranges from "classizied" editions (e.g. Avicenna Latinus) to the faithful reproduction of the exact orthography of a single medieval manuscript. These problems can be mitigated by radically removing all punctuation marks, changing all uppercase letters to lowercase letters und finally classizying the orthography. The last step is quite painfull for medievalist, but theres is no better alternative. As a first step, it is f.i. helpfull to replace all v with u and all j with i.

This process can be digitally enhanced by asking digital latin reference lexica if they can recognize words in the texts of the corpus. The easiest approach is the comparison with a latin word list. (f.i. here or in the word list of the OpenOffice lexicon, which can also be used in a Python script via PyEnchant) or the use of a morphology programm, which is able to lemmatize and kategorize every word in the text and look them up in a dictionary

For the latter, there are currently two open-source solutions:

  1. Whitaker’s Words, an Ada-based analysis programm for latin texts.
  2. Morpheus, the parser used by the Perseus program.

Both programs are quite complex and may often require some effort to compile correctly, especially if you want to integrate these programms into your own scripts using a wrapper. As an easier alternative, at least for some tests, the according web services (example) can be used as well. If the analysis program is configured correctly, it should be able to recogniza large portions of the texts as orthographically correct latin. Unrecognized words can be routinely replaced by their classical counterparts via a progressively adjusted ruleset. Usefull replacement rules are f.i. ci/ti, diff/def, ch/c etc., but also typical OCR mistakes like ic/it, ee/ec, b/h etc.

For a usable stylometric analysis, at least 95% of the words in the processed text should be recognized as correct latin by the reference lexica. However, 100% recognition should be the goal. To help with the correction of the latin texts, it may be advisable to program simple comparison and input masks, allowing the user to directly compare the words in question with the word in the original scan and correct them on the spot. Furthermore, it is advisable to expand the employed dictionariess by custom wort lists to cover the specific vocabulary of arabic-latin translations and the corresponding disciplines.


Once the texts are finally in an adjusted txt-format, the actual stylometric analysis can begin. The dataset can be devided in two groups, one with known and one with unknown translators. It´s important to keep up with the current state of research in this regard. When in doubt, the translation should rather be marked as "anonymus". For our research, we only accepted unambigous attributions found in the incipits and colophones of the manuscripts as reliable and marked all other texts as anonymous translations.

The corpus can be anylized with (at least) two different methods: By looking at words that are used exclusively by one of the known translators and by computerized analysis of the most frequent words (MFW) of a text. The first method has been developed at Würzburg University, the second is based on Burrows Delta (Burrows 2002).

(I) Exclusive Words

Experience has shown that anonymous translators can be identified by looking at frequently used words, that are used exclusively by a single known translators and that are not dependent on the text´s discipline. As an example, Dominicus Gundisalvi is the only translator wo uses 'sic ut, vel est, cuius comparatio, opus fuit, id per quod, id autem quod and omnis quod est, which can be also found in the anonymous translation of Alexander of Aphrodisias‘ De intellectu – a strong indication for Gundisalvi as the actual translator of the tractate. Getting there is a two-step process:

  1. The first step is searching for frequent terms that are used exclusively by a single translator. To this end, programing a simple search enginge is advisable. When filtering the word lists, flexible parameters can help to set a minimal frequency or the amount of texts that have to contain the word in question. To analyze word groups, the texts can be split into lists of n-grams (i.e. overlapping sequences of multiple words). Thus, the list of exclusive words can be reduced to typical and frequently used terms, f.i. words that appear at least in 10 works of the translator and in 40% of his translations. As an example, the term iterum quia appears in 4 of the 10 translations by Gerhards of Cremona in our philosophical corpus, where they are used a total of 56 times. Thus, iterum quia is both an exclusive and frequently used term in Gerhards work. Following a possible suspicion for a a false attribution, an additional parameter for error tolerance can be employded, admitting also words, that are used very rarely by other translators.
  2. In a second step, this list has to be filtered for content words specific to the text´s discipline, like substantia composita oder horoscopus. The remaining words a stilistic words in a more narrow sense, i.e. words that can be used in all scientific latin texts of this perios in principle. These may contain not only conjunctions and other particle words, but also words and phrases like examinatio, annullare or demonstrare voluimus. This focus is important, as experience has shown that content words are adopted by other translators more easily, whereas stilistic words and phrases appear more stable for one author only.

Subsequently, you can note for each anonymously translated text in the corpus which of these words appear in the text. If negative and positive evidence fit – meaning when a bunch of words exclusive to a single translator appear in the text (positive) and at the same time no exclusive words of other translators (negative), the attribution of the text to the known translator is quite certain.

For very short texts, it my be advisibale to expand the analysis to less frequent words. However, in this case, the less frequent words of other translators have to be compared as well. Experience shows, that only a huge amassment of these less typical words and phrases in an anonymously translated text allow for a credible attribution.

(II) Computergestützte Stilometrie mit Burrows Delta

Die zweite Methode basiert auf der Idee von John Burrows, dass Autorschaft computergestützt durch den Vergleich der standardisierten relativen Häufigkeiten der most frequent words (MFW) einzelner Texte ermittelt werden kann – ein Verfahren, das sich bei der computergestützten Autorschaftsattribuierung als ausgesprochen erfolgreich herausgestellt hat. Es gibt verschiedene frei im Web zugängliche Implementierungen dieses Verfahrens. Ein nutzerfreundliches Interface wird innerhalb des Stylo-R-Pakets von Maciej Eder und Jan Rybicki angeboten. Wir haben eine eigene Implementierung in Python verwendet, die auf Fotis Jannidis‘ pydelta aufbaut. In der Regel kann man bei solchen Implementierungen zwischen verschiedenen Abstandsmaßen („Deltas“) wählen, also zwischen verschiedenen Methoden, in denen der Computer den Abstand zwischen den Texten berechnet (bzw. genauer: den Abstand zwischen den Listen der Worthäufigkeiten der häufigsten Wörter berechnet). Vergleichsstudien der jüngsten Vergangenheit haben gezeigt, dass ein sehr performantes stilometrische Abstandsmaß das sogenannte „Cosine Delta“ ist. Auch wir haben die besten Ergebnisse mit Cosine Delta erzielt.

In einem ersten Schritt werden nur diejenigen Texte des Korpus analysiert, deren Übersetzer bekannt sind. Die Zahl der häufigsten Wörter, also 100, 200 oder mehr, lässt sich in den meisten Implementierungen einstellen. Wir haben sehr gute Ergebnisse mit den häufigsten 150 Wörtern der Texte erzielt. Jeder Text des Korpus wird intern durch einen Vektor dargestellt, der die standardisierten relativen Häufigkeiten dieser Wörter enthält. Der Abstand zwischen diesen Vektoren wird dann mit Cosine Delta berechnet. Der Computer formt dann Gruppen oder Cluster auf Basis dieser Abstände, die in einem Dendrogramm, einem Baum-Diagramm, visualisiert werden. Mithilfe dieses Verfahrens konnte der Computer im Korpus philosophischer Übersetzungen des 12. Jahrhunderts tatsächlich die Übersetzungen bekannter Übersetzer jeweils in eine Gruppe sortieren: die Gruppe der Übersetzungen des Dominicus Gundisalvi, des Gerhard von Cremona etc. Wenn das gelungen ist, ist die Methode sozusagen kalibriert.

In einem zweiten Schritt werden dann die anonymen Übersetzungen dazu gegeben. Das daraus resultierende Dendrogramm muss sorgfältig interpretiert werden: Bleibt die Gundisalvi-Gruppe (oder Gerhard-Gruppe etc.) des kalibrierten Standards stabil und wird nur um die ein oder andere anonyme Übersetzung erweitert, dann ist es sehr wahrscheinlich, dass diese anonymen Übersetzungen tatsächlich von Gundisalvi produziert wurden. Zerfällt aber die Gundisalvi-Gruppe (oder Gerhard-Gruppe etc.) in mehrere Teilgruppen, die im Dendrogramm nicht mehr verbunden sind, gelingt dem Computer offensichtlich die Zuweisung der anonymen Übersetzung nicht.

Bei unseren Versuchen zeigte aber erfreulicherweise, dass die Ergebnisse der Methode 1 (Exklusive Wörter) mit den Ergebnissen der Methode 2 (MFW) weitgehend übereinstimmten, zumindest beim philosophischen Korpus. Das astronomisch-astrologische Korpus ist für die Methode 2 allerdings noch nicht groß genug.

Language: Union Jack  Flagge der BRD