visualizing translation variation : shakespeare's othello

Visualizing Translation Variation : Shakespeare's Othello

Zhao Geng 1, Robert S. Laramee 1, Tom Cheesman 2, Alison Ehrmann 2 , David M. Berry

2,

1Visual Computing Group, Computer Science Department, Swansea University, UK, {cszg,r.s.laramee}@swansea.ac.uk

2College of Arts and Humanities, Swansea University, UK, {T.Cheesman,d.m.berry} @swansea.ac.uk, alison.ehrmann@t-

online.de

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OverviewIntroduction and MotivationRelated WorkBackground DataText Pre-processingStructure-aware TreemapParallel Coordinates For

Multi-Document ComparisonConclusionAcknowledgement

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IntroductionShakespeare's plays have been translated into dozens of languages for about

300 years

For every translation:

A different interpretation of the play

Reflect changing culture or express individual thought by the authors

Build a wide connection between different regions and reveal a retrospective

view of their histories

At the moment, researchers from Modern Languages collect a large number

of German translations of Shakespeare's play, Othello

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Motivation Goals of Visualization

Present different facets of the data Analyze the data in detail Explore the relationships and patterns to make new hypotheses

Complex Multi-Dimensional Data Set (translation, author, place, year, popularity)

Exploratory Specifications

Where, when, into which languages has Othello been translated ?

How have translators influenced one another ?

How do versions vary globally / locally ?

Which translation is more similar to the original play ?

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Related Work Multiple Document Visualization prototypes:

ThemeRiver [HHWN02] Parallel Tag Clouds [CBW09] DocuBurst [CCP09] SparkClouds [LRKC10]

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Related Work (Cont.)Ben Fry, “ On the Origin of Species: The Preservation of

Favoured Traces” (2009), http://benfry.com/traces

Lev Manovich, “Cultural Analytics: Visualizing Cultural Patterns in the Era of “More Media” http://manovich.net/articles/

Stephan Thiel, “Understanding Shakespeare: Towards a Visual Form for Dramatic Texts and Language ( 2010), http://www.understanding-shakepseare.com/

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Background Data ( Cont. ) 57 translations of Othello from 7 various countries, ranging from 1766 to 2006

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Text Pre-Processing Document CollectionDocument Standardization

In ASCII format stored in standard text editorTokenization

Break the stream of characters into words or tokensReduce the common wordsLanguage dependent

LemmatizationConvert to a standard formStemming to a root

ConcordanceTokens + Frequency

Vector Generation (LSI Model)

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LSI ModelLSI model ( Latent Semantic Indexing) : Tf ( Term Frequency) : the frequency of a term Θ occurs in a document.

Idf ( Inverse Document Frequency ): the inverse of the number of

documents a term Θ occurs in a document corpus.

The weight of a term Θ can be defined as :

W(Θ) = Tf(Θ) × Idf(Θ)

Each document D then becomes a vector :

D = ( W(Θ1), W(Θ2), … W(Θn) )T

Similarity between two documents D1 and D2 is measured by the angle of

two vectors:

cos Sim(D1,D2) = ( D1 D2) / (|D1| × |D2|)

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Structure-aware TreemapMeta Data Hierarchy

Century -> Decades -> Country -> Author->TitleVisualization

Treemap Aggregation of numerical values Re-ordering of hierarchies

DOI-Tree Structural clarity Initiate a searching task

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Structure-Aware Treemap

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Focus+Context Parallel Coordinates

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PC-Patterns (1)

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PC-Patterns (2)

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Video

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ConclusionInteractive system for exploring variation among different

German translations of Othello

Structure-Aware Treemap is developed for metadata analysis

Focus+Context Parallel Coordinates incorporate an objective similarity measure and allows the multi-document comparison

In the future, we will expand to the analysis of the whole play of Othello

Utilize more methods from computational linguistics to summarize more semantic feature

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Acknowledgement

Thanks for listening ! Any questions ?