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Visualizing Variation in aRe-Translation CorpusCase Study: William Shakespeares

Othello, The Moor of Venice

Pilot project funded by Swansea University College of Arts andHumanities Research Initiatives Fund (Feb July 2011)

PI: Tom Cheesman

David Berry Robert S. Laramee Andy Rothwell

Alison Ehrmann Zhao Geng


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Presentation by Tom Cheesman

at Dr Robert S. Laramees VISIBLE LUNCH talks series

Swansea University, Computer Science

http://www.cs.swan.ac.uk/~csbob/visibleLunch/

Thursday 10 February 2011


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>40 German Othellos (1766 2009) to be digitized:

pre-processing: i.e.: copy, scan, OCR, clean, &align with the English base text (tagging)

(and >30 French? >30 Italian? >40 Dutch

>30 Spanish? 6 Arabic )


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why explore Shakespeare re-translations?

Cultural History interest Each re-translation reflects changingculture* OR expresses individual thought [the translators]*

OR both: cultural / cultural-political interventions*

Cultural Observatory Versions currently produced (in print

and/or on stage, in media) intervene in current social, cultural,

political debates, such as: HamletorMacbeth: state politicsand the individual Othello: race/racism, gender, social

hierarchy, imperialism

Complexity: each re-translation is a reply* (a) to previous re-

translations, (b) to Shakespeares text, and (c) to received

ideas about what Shakespeare means William

Shakespeare as Global Icon and specifically William

Shakespeare as a German Writer

* Not (only) conscious / intentional


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background TC text corpus research

Recent study on Turkish German novels large corpus (c.90 texts),grouped and analysed using traditional methods (manual reading)

PhD on German ballads (1980s) in German Folksong Archive

large corpus of songs, each with >2,000 variants

variant: the same song as sung / written down / published... by different singers / collectors / editors

Variants grouped by common text features (e.g. presence/absence of

words, verses, story events)

Variationdimensions:Regions.

Historical periods.

Gender, Social Class, Religion of singer.

Source Media: singer manuscript / collector transcript / printed book.


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German Folksong Archive

250,000 typescript song transcriptions (fieldwork: mainly 1912-1930)

70,000 books with 1000s of song texts

20,000 sound recordings (mainly 1912-1930)

15,000 printed song sheets / pamphlets (15th - 20th c.)

20,000 song-type files


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Feridun Zaimoglu

+ Gnter Senkel


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Beginningto compare GermanOthellos

Zaimoglu and Senkel claim to have looked at about a dozen other translations

before writing their own adaptation.

In order to understand better what is special about their version, I decided to

look at Othellos by some of their precursors and competitors.

The Zaimoglu/Senkel version is very politically incorrect. A good example of

this is their translation of some sexist, misogynistic, and racist jokes told by

Iago (the villain in the play). The jokes are in rhyming couplets. Most

translators produce rhymes here. This puts the translators under abnormal

stress their choices are more limited than usual. And most translatorsmade the jokes sound relatively harmless.

But this series of jokes makes a very complex case study.


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For a first attempt to explore variation systematically in a small, conceptually

indicative textsample, I chose the Duke of Venices last words in the play.

Its a rhyming couplet (puts extra stress on translators)

It uses some of the plays key terms: virtue, delight, beauty, fair, black, and

Moor (more)

The Duke is speaking to Othellos father-in-law, Brabantio:

Ifvirtuenodelighted beauty lack,

Yourson-in-law isfarmorefairthan black.

Modern English versions (student cribs):

Ifvalouristhemeasureoftrue beauty, yourson-in-law isfairerthan

hes black. Shakespeare Made Easy(1989)

Ifgoodnessis beautiful, yourson-in-law is beautiful, not black.

No Fear Shakespeare (2003)


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Finding German Othellos

Blinn/Schmidt: Shakespeare deutsch. Bibliographie (2003)

www.theatertexte.de


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Germantranslations and adaptationsofOthello

consulted(NBthistableisnot up-to-date)(pre-1920: includes only those who translated the Dukes couplet

differently many in the 19th

c. copied Baudissin or Schiller here).Yellow fill = adaptation, not faithful translation


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The Dukes last couplet: Ifvirtuenodelighted beauty lack,


Wieland (1760s): wenn Tugend die glnzendeste Schnheit ist, so ist euer Tochtermann mehr weials schwarz.

Ifvirtueisthemost radiantbeauty, then yourson-in-law ismorewhite than black.

Schiller (1800s): Wenn je die Tugend einen Mann verklrt, / Ist Euer Eidam schn und liebenswert.

Ifever virtuetransfigureda man/ yourson-in-law isbeautiful and lovable.

Baudissin (1830s): Wenn man die Tugend mu als schn erkennen, / Drft Ihr nicht hlich EurenEidam nennen.

Ifonemustrecognise virtue as beautiful/ you may not callyourson-in-law ugly.

Gundolf (1900s): Entbehrt die Tugend Reiz und Schnheit nicht, / Ist euer Eidam minder schwarz alslicht.

Ifvirtuenot lack charm and beauty/ yourson-in-law is less black thanbright-lit.

Wolff (1920s): Leiht Tugend ihre Farbe dem Gesicht, / Ist Euer Eidam wei, ein Schwarzer nicht.Ifvirtuelends its colour to the face/ yourson-in-law is white, not a blackman.

von Zeynek (1940s): wenn Mannesmut nicht Reiz und Glanz entbehrt, / so ist er, wenn auch schwarz,hchst schtzenswert.

Ifmanly courage isnot without charm andradiance/glory/ thenheis, even ifblack, highlyestimable.


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Ifvirtuenodelighted beauty lack,


Laube (1970s): Wenn Tugend schn ist, hast du jetzt zum Lohn /Nen schwarzen, aber schnenSchwiegersohn.Ifvirtueis beautiful, you now have as your reward/wage/ a black butbeautiful son-in-law

Fried (1970s):Wenn Ihr der Tugend nicht Schnheit absprechen wollt, / Ist Euer Schwiegersohn nichtdunkel, sondern Gold!

Ifyou donotwish to denybeauty to virtue/ yourson-in-law isnotdarkbutgold!

Gnther (1990s):Gbs helle Haut fr Edelmut als Preis, / Dann wr Ihr Schwiegersohn statt schwarz

reinwei.Ifbright skin were aprize for noble-mindedness/ then yourson-in-law would be pure whiteinsteadofblack.

Wachsmann (2000s): Khnheit wirkt anziehnd, hell erstrahlt zum Lohn / Mehr schn als schwarzdrum Euer Schwiegersohn.

Boldness affects [us as] attractive, brightly shines as a reward/ more beautiful than blacktherefore yourson-in-law.

Zaimoglu/Senkel (2000s):Solange mnnliche Tugend mehr zhlt als Schnheitsfehler, kann mansagen, Ihr Schwiegersohn ist eher edel als schwarz.

So long asmale virtue countsmorethanminor blemishes [literally: beauty-failings], onecansay yourson-in-law ismorenoble than black.

http://www.delightedbeauty.org


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The Dukes couplet: Germantranslators choicesfor lexical andsyntactic

features (horizontal rows);1760s 2000s

Normalchoices for source text features: found by counting.

Many departures from the norm = a hot translation.

>Temperature range: white (very normal / cold) through blue (average) ,

to green, to orange, to red (very abnormal).

(Pastel colours = similar features found in specific historical periods.)

>


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www.delightedbeauty.orgShakespeare's Global Rewritings

Matthias Zach (Nantes) collected c.30 French versions of the

couplet.

Further crowd-sourcing limited success so far but we do have

some Albanian, Norwegian, Spanish, Italian versions of the

couplet.

What next?

Sampling basedonintuitionmay not be the best approach

to discover how versions inter-relate, copy one another, differfrom one another, and interpret the play in differing ways.

Before attempting global multilingual exploration, lets

refine methods.

But first, one finding of the couplet analysis


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Author-Translators and OtherTranslators

German and French Authors (= famous writers/translators)

translate fair and black in the Dukes couplet differentlyfrom non-Authors

but German Authors translate more differently,

relative to non-authors in the same languageAPOLOGIES FOR THE UNINTUITIVE GRAPHIC!


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Wholetext:intrinsic structuring Scenes / Characters (parts)

alsostructural: Sentences. Speeches.Rhymed verse Blank verse Prose.

Soliloquy Duologue Multilogue.

Words (lemmas?) / Semantic fields (?)


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Tagged basetext scenes, characters,

numberedspeeches, andspecial features

also:


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Tools- ?

SURVEY oftext visualizationtools by the Zhao Geng / our ProjectTeam:http://cs.swan.ac.uk/~cszg/text/Survey.pdf

DH: keep up athttp://www.digitalhumanities.org/ SeeespCraig:Stylistic Analysis and Authorship Studies, in Companion to Dig Hums, ch.20,www.digitalhumanities.org/companion

Susan Schreibmans Versioning Machine: www.v-machine.org/

multiple witnesses e.g. Emily Dickinson, There are two [or three]ripenings:

www.v-machine.org/samples/fp420.html

Scholarly Editions: www.sd-editions.com/ + InterEdition (an international initiativefor digital scholarly editing infrastructure, an EU COST Action): www.interedition.eu/

Textometric analysis of multilingual text corporaDr Zimina Maria, Centre de Textomtrie SYLED-CLA2T, Paris Sorbonne

Paper: http://www.corpus.bham.ac.uk/pclc/CL_05_Zimina_M_005.doc

www.cavi.univ-paris3.fr/ilpga/ilpga/tal/lexicoWWW/index-gb.htm

Automated alignment, mkAlign: http://tal.univ-paris3.fr/mkAlign/mkAlignDOC.htm

Askis (Bergen) Translation CorpusAligner: http://gandalf.aksis.uib.no/tca2/index.page

Screenshot: https://reader009.{domain}/reader009/html5/0523/5b050b890059d/5b050b954a24English-Norwegian Parallel Corpus Translation CorpusExplorer:

http://khnt.hd.uib.no/webtce.htm


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Matthew L Jockers (Stanford) uses Docuscope to count high-frequency words and

punctuation marks in order to machine-sort texts into genres: e.g. 36 19th-century

novels into 12 genres, or : Shakespeares plays into3

genres.http://www.stanford.edu/~mjockers/cgi-bin/drupal/node/27


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Equipped with a specialized dictionary, Docuscope is able to divide texts into strings of

words that are then sorted into one of eighteen word categories, such as "Inner

Thinking" and "Past Events." The program turns differentiating amongst genres into astatistical task by testing the frequency of occurence of words in each of the

categories for each individual genre and recognizing where significant differences

occur.

Docuscope was designed as a tool for analyzingstudent writing, but Witmore (et.

al.) discovered that it could also be employed as a specialized sort of feature

extraction tool. - Jockers

[Could differentiating amonggenres besimilarto differentiating among

translations? TC]

Mike Witmore(Working Group for Digital Inquiry, Wisconsin) and Jonathan Hope

(Strathclyde):

In this essay, we explore the underlying linguistic matrix of Shakespeares dramatic

genres using multivariate statistics and a text tagging device known as Docuscope, ahand-curated corpus of several million English words (and strings of words) that have

been sorted into grammatical, semantic and rhetorical categories.

-- http://winedarksea.org/?p=707

And see other essays by Witmore e.g. Texts as Objects II:

http://winedarksea.org/?p=381


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?? Concordancing software (compare concordance outputs?)

Understanding Shakespeare by Stephan Thiel Potsdam:

http://www.understanding-shakespeare.com/about.html - using

Classifier4J (http://classifier4j.sourceforge.net/)

> Beautiful and useful!

> Summarizing approach: Segmentation of text by scene & part

& speech. Statistical analysis to find most typical sentence

in each speech. (Could also be any string of words or [140]

characters?) Will summaries of

DATA: digitally assisted text analysis blog by Martin Mueller at

http://literaryinformatics.northwestern.edu/node/1


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Turnitin

Input so far: in sequence, the already-digital German Othello texts: Wieland

(1766), Baudissin (1832), Zaimoglu & Senkel (2003), Wachsmann

(2005)

(We also have 6 others in protected pdf formats)

Expectations:

B takes 1%-5% from W.

Z takes 0% from W and 1%-5% from B.

Wa takes 5%-20% from B.

Many others take >90% from B and/or from subsequent versions based on

B.

Can Turnitin output be neatly visualized? To construct a genetic

tree? Map this onto geographical data (place of publication)?


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Desideratum (long-term):historical atlas asinterfaceforexploring

when and where Shakespeares work has beentranslated,

zoominginonindividual languages, orplaces, orperiods, or

plays, thengettingdetailsofhow eachtranslationinterpretedthe work by part, scene, speech, or wordorsemantic field

FOR NOW,

Some tools are language-specific (based on an English text corpus).

Few tools assist comparison between texts.

Editioning software (collation of variants)?

Statistical analysis based on concordances of aspects of the text

creates a digest for speed-reading. Digests of:: the whole play / a

scene / a speech / a characters part (we decide). Thesedigests

can then be compared / the comparison visualized - ?

Lemmatization if manual, to be avoided (immense labour). But seehttp://llc.oxfordjournals.org/content/25/3/287.short?rss=1

Following slides: IBMs Many-Eyes visualisation packages:

http://manyeyes.alphaworks.ibm.com/manyeyes/


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Data file: Most Common Word Pairs to Follow 'Tis' in Shakespeare

Data source: Project Gutenberg's Shakespeare Plays

but the / the better / all one / but a / just to / dimpled. I / true; he / this fever / like a / Agamemnon just. /

Nestor / right. / for Agamemnon's / dry enough-will / most meet. / meet Achilles /


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Wordles (Macbeth and The Sonnets)


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Jane Austen's Pride and Prejudice : Phrase Nethttp://manyeyes.alphaworks.ibm.com/manyeyes/page/Phrase_Net.html


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Shakespeare's 2-word images.

Phrase Net based on The Sonnets


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Phrase Net: < s > in the Sonnets


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above, Othello 3.3 (Word Cloud)

below left, words spoken, by character and by scene

below right, Word Tree


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Graphs from Franco MorettisBOOK:Graphs, Maps, Trees

Timelines showing production of differing literary genres etc


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From: https://reader009.{domain}/reader009/html5/0523/5b050b890059d/5


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Visualisationsofbook contents

http://flowingdata.com/2008/06/12/12-cool-visualizations-to-

explore-books/

http://www.textarc.org/

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