1 / 18

Visualizing Translation Variation : Shakespeare's Othello

This study explores the diverse interpretations of Shakespeare's "Othello" through visualization techniques. With 57 translations collected from 1766 to 2006, this project highlights how different translations mirror cultural contexts and individual translator perspectives. We employ a structure-aware treemap and parallel coordinates for multi-document comparisons, revealing relationships and patterns among the translations. Ultimately, this research aims to enhance the understanding of translation variations and their impact on the interpretation of classic texts like "Othello."

jaeger
Download Presentation

Visualizing Translation Variation : Shakespeare's Othello

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Visualizing Translation Variation : Shakespeare's Othello Zhao Geng 1, Robert S. Laramee 1, Tom Cheesman 2, Alison Ehrmann2 , 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

  2. Overview • Introduction and Motivation • Related Work • Background Data • Text Pre-processing • Structure-aware Treemap • Parallel Coordinates For Multi-Document Comparison • Conclusion • Acknowledgement

  3. Introduction • Shakespeare'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

  4. 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 ?

  5. Related Work • Multiple Document Visualization prototypes: • ThemeRiver [HHWN02] • Parallel Tag Clouds [CBW09] • DocuBurst [CCP09] • SparkClouds [LRKC10]

  6. 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/

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

  8. Text Pre-Processing • Document Collection • Document Standardization • In ASCII format stored in standard text editor • Tokenization • Break the stream of characters into words or tokens • Reduce the common words • Language dependent • Lemmatization • Convert to a standard form • Stemming to a root • Concordance • Tokens + Frequency • Vector Generation (LSI Model)

  9. LSI Model LSI 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: cosSim(D1,D2) = ( D1  D2) / (|D1| × |D2|)

  10. Structure-aware Treemap • Meta Data Hierarchy • Century -> Decades -> Country -> Author->Title • Visualization • Treemap • Aggregation of numerical values • Re-ordering of hierarchies • DOI-Tree • Structural clarity • Initiate a searching task

  11. Structure-Aware Treemap

  12. Focus+Context Parallel Coordinates

  13. PC-Patterns (1)

  14. PC-Patterns (2)

  15. Video

  16. Conclusion • Interactive 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

  17. Acknowledgement Thanks for listening ! Any questions ?

More Related