Visualizing Translation Variation: Shakespeare's Othello.
ABSTRACT Recognized as great works of world literature, Shakespeare's poems and plays have been translated into dozens of languages for over 300 years. Also, there are many re-translations into the same language, for example, there are more than 60 translations of Othello into German. Every translation is a different interpretation of the play. These large quantities of translations reflect changing culture and express individual thought by the authors. They demonstrate wide connections between different world regions today, and reveal a retrospective view of their cultural, intercultural, and linguistic histories. Researchers from Arts and Humanities at Swansea University are collecting a large number of translations ofWilliamShakespeare's Othello. In this paper, we have developed an interactive visualization system to present, analyze and explore the variations among these different translations. Our system is composed of two parts: the structure-aware Treemap for document selection and meta data analysis, and Focus + Context parallel coordinates for in-depth document comparison and exploration. In particular, we want to learn more about which content varies highly with each translation, and which content remains stable. We also want to form hypotheses as to the implications behind these variations. Our visualization is evaluated by the domain experts from Arts and Humanities.
- SourceAvailable from: Scott Bateman[Show abstract] [Hide abstract]
ABSTRACT: Tag clouds are a popular method for visualizing and linking socially-organized information on websites. Tag clouds represent variables of interest (such as popularity) in the visual appearance of the keywords themselves - using text properties such as font size, weight, or colour. Although tag clouds are becoming common, there is still little information about which visual features of tags draw the attention of viewers. As tag clouds attempt to represent a wider range of variables with a wider range of visual properties, it becomes difficult to predict what will appear visually important to a viewer. To investigate this issue, we carried out an exploratory study that asked users to select tags from clouds that manipulated nine visual properties. Our results show that font size and font weight have stronger effects than intensity, number of characters, or tag area; but when several visual properties are manipulated at once, there is no one property that stands out above the others. This study adds to the understanding of how visual properties of text capture the attention of users, indicates general guidelines for designers of tag clouds, and provides a study paradigm and starting points for future studies. In addition, our findings may be applied more generally to the visual presentation of textual hyperlinks as a way to provide more information to web navigators.Proceedings of the nineteenth ACM conference on Hypertext and hypermedia; 01/2008
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ABSTRACT: We discuss the design and usage of "Wordle," a web-based tool for visualizing text. Wordle creates tag-cloud-like displays that give careful attention to typography, color, and composition. We describe the algorithms used to balance various aesthetic criteria and create the distinctive Wordle layouts. We then present the results of a study of Wordle usage, based both on spontaneous behaviour observed in the wild, and on a large-scale survey of Wordle users. The results suggest that Wordles have become a kind of medium of expression, and that a "participatory culture" has arisen around them.IEEE Transactions on Visualization and Computer Graphics 11/2009; 15(6):1137-44. · 1.92 Impact Factor
- Computer Graphics Forum 06/2009; 28(3):1039-1046. · 1.60 Impact Factor
Visualizing Translation Variation: Shakespeare’s Othello
Zhao Geng1, Robert S. Laramee1, Tom Cheesman2, Alison Ehrmann2,
and David M. Berry2
1Visual Computing Group, Computer Science Department, Swansea University, UK
2College of Arts and Humanities, Swansea University, UK
Abstract. Recognized as great works of world literature, Shakespeare’s poems
and plays have been translated into dozens of languages for over 300 years. Also,
there are many re-translations into the same language, for example, there are
more than 60 translations of Othello into German. Every translation is a different
interpretation of the play. These large quantities of translations reflect changing
culture and express individual thought by the authors. They demonstrate wide
connections between different world regions today, and reveal a retrospective
view oftheir cultural,intercultural,andlinguistichistories.ResearchersfromArts
and Humanities at Swansea University are collecting a large number of transla-
tions of WilliamShakespeare’s Othello. Inthispaper, we havedeveloped an inter-
active visualization system to present, analyze and explore the variations among
these different translations. Our system is composed of two parts: the structure-
aware Treemap for document selection and meta data analysis, and Focus + Con-
text parallel coordinates for in-depth document comparison and exploration. In
particular, we want to learn more about which content varies highly with each
translation, and which content remains stable. We also want to form hypotheses
as to the implications behind these variations. Our visualization is evaluated by
the domain experts from Arts and Humanities.
William Shakespeare is widely regarded as one of the greatest writers and his plays
have been translated into every major living language. This is a historical and contem-
poraryphenomenon.In German, the first translation of one play, Othello, was produced
in 1766. By now there are over 60 translations including 7 new translations of this play
which have been produced since the year 2000. Questions about these translations are
seldom asked in the Anglophone world, because interpreting them is difficult without
specialist linguistic and cultural knowledge. The original Shakespeare’s work in En-
glish is normally considered more important than any translations. But with increasing
awareness of global cultural interconnections, more Arts and Humanities researchers
recognize the significance of translations and are investigating them.
The interpretation of Shakespeare’s work in translation is always influenced by the
translator’sownculture,customs andconventions.Therefore,eachtranslationis a prod-
uct of changing culture as well as an expression of each translator’s individual thought
G. Bebis et al. (Eds.): ISVC 2011, Part I, LNCS 6938, pp. 657–667, 2011.
c ? Springer-Verlag Berlin Heidelberg 2011
658Z. Geng et al.
within that culture. Also, each translation is a reply to receivedideas about what Shake-
carry relational cultural significance. Normally, researchers from Arts and Humanities
read and compare cultural text in its raw form and this makes the analysis of the multi-
ple translations difficult. In addition, interesting patterns are often associated with text
metadata, such as historical period, place, text genre or translator profession.
Up until now,researchersfromArts andHumanitieshave collectedmorethan50 dif-
ferent versions of German translations of Shakespeare’s play,Othello. Our general goal
is to identify similarities and differences among these translations. Compared to tradi-
tional text mining, text visualization incorporates the visual metaphors and interactive
design to facilitate in-depth exploratory data analysis.
In this paper, we aim to develop an interactive visualization system to help the re-
searchers from Arts and Humanities perceive and understand their collected German
translations in new ways. In order to do so, we collect a large amount of metadata as-
sociated with the original documents and extract semantic features from the document
contents. Based on such extracted information, various visualizations can be applied.
We propose a structure-aware Treemap for metadata analysis and document selection.
Once a group of documents are selected, they can be further analyzed by our Focus +
Context parallel coordinates.
The rest of this paper is organized as follows: In Section 2, we review the previous
work on text visualization. In Section 3, we describe our source data. In Section 4, we
explain how are the original documents processed before being input to the visualiza-
tion. In Section 5, we illustrate our structure-aware Treemap for meta data analysis. In
Section 6, we present the Focus + Context parallel coordinates for translation variation
exploration. In Section 7, we report the feedback from the domain experts. Section 8
wraps up with the conclusion.
2 Related Work
Since 2005, from the major visualization conferences, we can observe a rapid increase
in the number of text visualization prototypes being developed. As a result, various vi-
sual representations for text streams and documents are proposed to effectively present
and explore the text features.
A large number of visualizations have been developed for presenting the global pat-
terns of individual document or overviews of multiple documents. These visualizations
are able to depict word or sentence frequencies, such as Tag Clouds , Wordle ,
WordTree , or relationships between different terms in a text, such as PhraseNet ,
TextArc  and DocBurst . The standard Tag Clouds  is a popular text visual-
ization for depicting term frequencies. Tags are usually listed alphabetically and the
importance of each tag is shown with font size or color. Wordle  is a more artisti-
cally arranged version of a text which can give a more personal feel to a document.
ManiWordle  provides flexible control such that the user can directly manipulate the
original Wordle to change the layout and color of the visualization. Word Tree  is a
visualization of the traditional keyword-in-contextmethod. It is a visual search tool for
unstructured text. Phrase Nets  illustrates the relationships between different words
Visualizing Translation Variation: Shakespeare’s Othello
used in a text. It uses a simple formof pattern matchingto providemultiple views of the
concepts contained in a book, speech, or poem. A TextArc  is a visual representation
of an entire text on a single page. It provides animation to keep track of variations in
the relationship between different words, phrases and sentences. DocuBurst  uses a
radial, space-filling layout to depict the document content by visualizing the structured
text. The structured text in this visualization refers to the is-kind-of or is-type-of rela-
tionship. These visualizations offer an effective overview of the individual document
features, but they cannot provide a comparative analysis for multiple documents.
In contrast to single documentvisualizations, there are relatively few attempts to dif-
ferentiate features among multiple documents. Noticeable exceptions include TagLine
Generator, Parallel Tag Clouds , ThemeRiver and SparkClouds . Tagline
Generator  generates chronological tag clouds from multiple documents without
manual tagging of the data entries. Because the TagLine Generator can only display
one document at a time, it is unable to reveal the relationships among multiple doc-
uments. A much better visualization for this purpose is Parallel Tag Clouds . This
visualization combines parallel coordinates and tag clouds to provide a rich overview
of a document collection. Each vertical axis represents a document. The words in each
documentare summarized in the form of tag clouds alongthe vertical axis. When click-
ing on a word, the same word appearing in other vertical axes is connected. Several
filters can be defined to reduce the amount of text displayed in each document.One dis-
advantage of this visualization is its incapability to display groups of words which are
missing in one documentbut frequentlyappearin the others. When we explorethe vari-
ations among the Othello translations, the domain experts would like to know groups
of words which a particular author never uses but which frequently appear in other
authors’ work. Also, brushing multiple words in different documents might introduce
clutter due to the crossing lines in parallel tag clouds.
We also observe some interesting visualizations which can depict time trends over
different documents. SparkClouds  integrates sparklines into a tag cloud to convey
trends between multiple tag clouds over time. Results of a controlled study that com-
pares SparkClouds with traditional trend visualizations, such as multiple line graphs,
stacked bar charts and Parallel Tag Clouds, show that SparkClouds is more effective at
showing trends over time. The ThemeRiver  visualization depicts thematic varia-
tions overtime within a large collectionof documents.The thematic changesare shown
in the context of a time line and correspondingexternal events. This is the first work, to
our knowledge, that compares multiple translations of a single play.
3Background Data Description
The domain experts from Arts and Humanities have collected 57 different German
translations of Shakespeare’s play, Othello. For each translation, metadata recorded in-
cluding Germany (pre-1949),East Germany (1949-1989),West Germany (1949-1989),
FRG (Germanysince 1989),Austria, Switzerlandand England.Theimpact indexrefers
to each translator’s productivity and reputation. it includes the re-publication figures or
660Z. Geng et al.
Fig.1. This image illustrates the distribution of our collected German Othello translations. The
X-axis is mapped to the publication date and Y-axis to seven different countries. The dot size
is mapped to the impact index. A larger radius depicts a translation with higher re-publishing
the translator is not listed in the bibliography and 5 means that more than 50 publica-
tions and re-publications by the translator are listed in the bibliography.Figure 1 shows
the chronological distribution of our collected documents. The X-axis is mapped to the
publication date and Y-axis to the different countries. The ellipse radius is mapped to
4 Text Preprocessing
Before the original translation can be analyzed within our visualizations, we need to
generate various features from the textual information and transform them into numer-
ical vectors. In this work, we process our original text in five steps, namely document
standardization, tokenization, stemming, vector generation and similarity calculation.
The major outputs include making concordance of each document and computing their
Since the Othello translations are collected from various sources (some PDF, some
archival typescripts, mostly books), we firstly transform and integrate them into a stan-
dard XML format. Next, document tokenization breaks the stream of text into a list of
individual words or tokens. During this process, common words carrying little mean-
ing which are not of interest to domain experts, such as ”der” (the), ”da” (that) etc, are
eliminated from the token list. Furthermore, stemming reduces all of the tokens to their
root forms. Based on this cleaned and standardized token list, we are able to generate a
concordancetable for each document by counting the frequency of every unique token.
For in-depth document comparison, we also need an objective document similarity
keywords as a search query. This keyword list can be extracted from multiple interest-
ing documents. The similarity between our collected translations can then be measured
Visualizing Translation Variation: Shakespeare’s Othello
using the LSI (Latent Semantic Index) model . This model is widely used in in-
formation retrieval where the list of terms associated with their weight is treated as the
document vectors. The weight of each term indicates its importance in a document, and
is given by T f ×Idf. We use Tf (Term Frequency) to refer to the number of times a
term occurs in a given document, which measures the importance of a word in a given
document. Idf (Inverse Document Frequency),as its name implies, is the inverse of the
Document Frequency. The Document Frequency is the number of documents in which
a word occurs within the collection of documents.
Thus the weight of a term i in document j can be defined as:
wi,j=t fi,j×idfj=t fi,j×logN
where N is the total number of documents in the corpus, df is the document frequency
and idf is the inverse document frequency. Large values of wi,jimply term i is an im-
portant word in document j but not common in all documents N.
Then a document j can be represented as a vector with each dimension replaced by
the term weight:
Dj= (w(0, j),w(1, j),....,w(n, j))T
A large number of words in the search query might lead to extremely high-dimensional
document vector, so we use the SVD (Singular Vector Decomposition) to perform a
dimension reduction. Then the similarity between the two documents j and k can be
measured by the angle between these two vectors:
cos Sim(Dj,Dk) =
Such similarity measures are generated for all of our Othello translations. This infor-
mation is featured in our treemap and parallel coordinates.
5 Structure-Aware Treemap
As discussed in Section 3, metadata of each document includes author name, play title,
date,place ofpublicationandimpactindex.Thescatterplotin Figure1 is ableto present
the overall historical distribution, but it cannot provide an aggregation of the data. For
example,if theuser wants to exploreorrankthetotal numberoftranslations,orthe total
numberof re-publicationsin anycentury,decadeorcountryin ourdocumentcollection,
the scatterplot is unableto conveyan answer. Next to this, we observethat the meta data
can be arrangedin a hierarchical structure. For example, each century breaks down into
several decades. In each decade a few translations are published in several countries.
In each country several authors published their work. For each author his translations
have the impact index. Given this structure, we are able to generate a Treemap [14,15]
The traditional treemap is able to compare the node values in any tree level. But it
lacks the ability to show the entire tree structure intuitively. For tracing the treemap
662Z. Geng et al.
Fig.2. This image illustrates the interface of our structure-aware treemap. The left part shows the
control panel by which the user is able to manipulate the tree hierarchy, compare the values in
each hierarchy via a bar chart and set up the configuration for the visualization. Also the user is
able to select their interesting documents from the spreadsheet. The right part shows the treemap
and DOI-tree. The area of the leaf node is mapped to the quantity. As we drill down and up to
different tree levels, the DOI-Tree keeps track of the structure. Also, the DOI-tree could initiate
a searching task.
hierarchy, it’s necessary to only list the relevant substructure which shows the ancestor
and descendants of the interested node. The Degree-of-Interest tree  provides a
clear hierarchy at a low cost of screen space by changing the viewpoint and filtering
out the uninteresting tree nodes. In addition, it offers instant readability of the node
labels. Therefore, we adopt linked views using both DOI tree and treemap to enable
structure tracing. Our system is composed of two parts, namely the control panel and
structure-aware treemap. The control panel is shown on the left half of Figure 2. It
extracts the ontological hierarchy information from the input data sets and sets up the
configuration for the visualization. The user is able to change the order of hierarchy or
reduce the number of hierarchies by moving the graph nodes. The right half of Figure
2 is a structure-aware hierarchical visualization, containing the coordinated views of
the squarified treemap and DOI tree . As we traverse back and forth between the
intermediate levels of the treemap, the DOI tree view clearly keeps track of how each
selected node is derived from its ancestors.
The area of the leaf node can be either mapped to the impact index, the similarity
measure or the quantity. In Figure 2, from the bar chart, we learn that most of our
collectedtranslations were publishedin the twentieth century.Duringthis century,most
translations are publishedin the 1940sand 1970s.For the domainspecialists, this raises
questions about possible correlations with comparable datasets (translations of other
or all Shakespeare plays), and about possible correlations between periods in German
history, and specific interest in Othello. By changing the hierarchy, we also learn that
Visualizing Translation Variation: Shakespeare’s Othello
Fig.3. This image shows an overview of our visualization. The parallel coordinates illustrates a
focus view of the term frequency. The text boxes below the parallel coordinates show the context
views. They present the entire sentences from the original text where each keyword appears.
although the documents are all translations of Othello, they have different titles: the
commonest titles of the translations are ”Othello” or ”Othello, der Mohr von Venedig”,
some authors use the title ”Die Tragdie von Othello, dem Mohren von Venedig”, two
use ”Othello, der Maure von Venedig” and one author uses the title ”Othello, Venedigs
Neger”. These outliers are particular interest to the domain experts.
Our treemap system helps users manage their documents, such as ranking the docu-
ments according to different criteria, analyzing the global features of the metadata and
selecting the interesting documents. It can be scaled up to include new datasets such as
translations of other works by Shakespeare and enable users to explore common pat-
terns in the metadata. The DOI tree can initiate the searching task by which a user is
able to search terms in any hierarchy. Since the collection of our German translations
is still expanding, our treemap will play an increasingly important role in the meta data
6 Focus+Context Parallel Coordinates
Parallel coordinates, introduced by Inselberg and Dimsdale [17,18] is a widely used
visualization technique for exploring large, multidimensional data sets. It is powerful
in revealing a wide range of data characteristics such as different data distributions and
functionaldependencies. As discussed in Section 4, the textual informationof each
document can be transformed into a vector. In our parallel coordinates, we encode the
document dimensions as term frequencies.
664Z. Geng et al.
Fig.4. In this image, we obtain five keywords which only appear once in all documents
Domain experts from Arts and Humanities selected eight interesting translations ac-
the play, Othello’s big speech to the Venetian Senate in Act1, Scene3: the longest single
speech in the play (about300 words in Shakespeare’s text). Figure 3 shows an overview
are most frequently occurring significant words in the document corpus. The parallel
coordinates present a focused view of keyword frequencies. Each document is repre-
sented by a vertical axis. In order to maintain a unified scale, the height of each vertical
axis is made proportional to the range between each document’s minimal and maximal
word frequencies.Zero frequencysimply means that a keywordhas not occurredin that
document. The thickness of each vertical axis is mapped to the document’s similarity
with others in terms of LSI score: a thicker line means a higher similarity value. The
number of occurrences of each keyword in each document is connected by a polyline.
Each polyline is rendered in a different color to enable visual discrimination. The text
boxes below the parallel coordinates provide context views for keywords selected by
the user. Each text box represents an individual document and shows the entire sen-
tences from the original text where each selected keyword occurs. We also apply the
edge bundling to enhance the visual clustering and user is able to control the curvature
of the edge . Curves with the least curvature become a straight line.
We provide various interaction support, such as selection, brushing and linking. As
the user selects individual or multiple keywords, the corresponding polylines are ren-
dered. The user can also select various frequencylevels in any document and the corre-
sponding keywords having that frequency are displayed. Along with the selection and
brushing, the text boxes which show the context views keep updating.
Visualizing Translation Variation: Shakespeare’s Othello
Fig.5. In this image, there are two keywords showing a strong correlation
We can use the AND-Brush to obtain all keywords which occur in every document:
words used by all translators regardless of the translators’ reputations and impact. If
we brush the keywords which do not appear in document ”Baudissin 1958”, we learn
that this document contains all the keywords except ”fand”. This helps to explain why
this document has the highest similarity score. The domain experts indicates that this
finding is surprising and interesting. As shown in Figure 4, we observe five keywords
which appear just once in all the documents. From the context views, the sentences
containingthese twowordsarealmostthesame ineverytranslation.As showninFigure
5, there are two keywords showing a strong correlation. Both findings raise interesting
questions for the domain experts.
7 Domain Expert Reviews
The focus+context parallel coordinates permits comparative visualization and explo-
ration of concordances. A concordance is normally displayed as a simple list of words
in a vertical column (in order of frequency or alphabetically). Standard concordance
software also offers the option to display contexts of use for a particular word (i.e.
the different word strings in which a word appears). This tool successfully combines
a concordance-derived keyword list and context views with display of frequencies of
words across multiple, comparable versions, in the form of parallel coordinates. This is
a promising way of exploringtexts through their different uses of meaningfulwords. In
the display of parallel coordinates, the composite brushing enables us filter for any cor-
relations between word-uses, positive or negative: pairs/groups of words which appear
666 Z. Geng et al.
together,or never appear together. The similarity of each document tells us an objective
measure of how similar each document is to the keyword lists. In this particular case,
the visualization tells us that Baudissin’s translation-which is the standard, most often
republishedandperformedGermantranslationof the play - containsthe most keywords
in this speech which are common to most of the other translations. Since other trans-
lations are produced and marketed as ”alternatives” to Baudissin, this high degree of
apparent dependency on the standard translation is surprising, and it demands further
plays, and poems; and potentially works by other writers) and also in respect of other
languages of translation (at least one of Shakespeare’s works exists in about 100 lan-
guages). Hence, the flexible metadata overviewofferedby the structure-awareTreemap
visualization will become increasingly valuable in managing the dataset, exploring its
various dimensions and selecting subsets of translations for further analysis.
In this paper, we describe an interactive visualization system for presenting, analyzing
and exploringthe variation among different German translations of Shakespeare’s play,
Othello. A structure-aware treemap is developed for metadata analysis and the focus +
context parallel coordinates is developed to investigate the variations among the trans-
lations. Our parallel coordinates incorporate an objective similarity measure for each
document using LSI model. Also, various interaction supports are realized to facilitate
the information seeking mantra: overview first, zoom and filter and detail on demand.
Our visualization is evaluated by the domain experts from Arts and Humanities. Be-
cause it is just the beginning of our project, in the future, we would like to add more
advanced features to the parallel coordinates, such as visual clustering. Also, we will
keep on collecting more translations. Further statistical and linguistic analysis will be
Acknowledgments. This studywas fundedbySwansea University’sResearch Institute
for Arts and Humanities (Research Initiatives Fund). The conference trip is supported
by Computer Science Department of Swansea University. We are grateful to ABBYY
Ltd for allowing us to use their unique Optical Character Recognition package which
can handle the old German Fraktur font, which is used in many of the Othello books.
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