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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,
Yourson-in-law isfarmorefairthan black.
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,
Yourson-in-law isfarmorefairthan black.
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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