1. DocuBurst:
Visualizing Document Content
Using Language Structure
EuroVis 2009 Christopher Collins, Sheelagh Carpendale, and Gerald Penn

2. 2

3. 3

4. Document Content Visualization
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 Navigation in collections of digital text
 Content analysis (digital humanities)
 Plagiarism detection
 Authorship attribution

5. ...Using Language Structure
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 Traditional glyph techniques use unstructured
word counts (e.g. tag clouds)
 DocuBurst structure is based on a carefully
designed ontology called WordNet

6. WordNet Background
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 Basic data unit is a set of synonyms called a synset:
{lawyer, attorney}, {jump, hop, skip}
 Words can occur in multiple synsets:
{bank, financial institution}
{bank, slope, riverside}
 Free resource from Princeton University

7. Hyponymy Relation
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 X is a Y or X is a kind of Y
 transitive, asymmetric relationship
 example
 {robin,redbreast} IS A {bird}
 robin and redbreast are hyponyms of bird
 forms the basic structure of the noun network
{robin, redbreast} IS-A {bird} IS-A
{animal, animate_being} IS-A
{organism, life_form, living_thing} IS-A {entity}

8. Creating DocuBurst
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gamesgame
takentake
absolute,noun,10
chair,noun,2
moment,noun,11
game,noun,30
reality,noun,3
take,verb,13
represent,verb,17
...
game IS activity
chair IS furniture

9. Hyponymy Structure

10. Word Sense Ambiguity
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 Man = {mankind,world}, {male human}, ...
 Water = {H2O}, {water supply}, {body of water}, ...
 Word senses are roughly ordered by frequency in
WordNet

11. Alternative Scoring Models
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 Count for all senses
 undue prominence to ambiguous words
 Count first sense only
 loses too much information
 Divide by sense count (same for all senses)
 high penalty on polysemous words
 Divide by sense index
 decreased prominence for uncommon senses

12. Visual Encoding
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 Node Size: # of leaves in subtree
 Stability across documents
 Node Position: IS-A relation
 Multi-level linguistic abstraction
 Additive
(2 ducks + 3 geese = 5 birds)
 Node Hue: sense index
 Differentiates subtrees
 Node Saturation: word count
 Ordering & approximate scale is perceived
 Node Label: First word in synset
 Words are ordered by commonality in the
language, reveals well-known words

13. Node Colouring Alternatives
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Cumulative Counts Single Node Counts
Supports Visual Summaries Supports Precision and Selection

14. 14 Interaction

15. Trace-to-Root
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Cattle IS-A bovine IS-A bovid IS-A ... Mammal IS-A vertebrate IS-A chordate IS-A animal

16. Roll Up
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17. Drill Down
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18. 18

19. 19

20. Concordance
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21. Level of Detail Filter
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 Nodes > N away from root are hidden

22. Search
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23. 23 Design Trade-Offs

24. Node Size Mapping
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 Size by # leaves
+ consistent
– visual artifacts (highly relevant words with few leaves
are too small)
 Size by score
+ redundant encoding
+ important words more prominent
– disrupts inter-document comparison

25. Font Size Mapping
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 Size to fit cell
+ maximize legibility
– short words have huge font
 Font size proportional to cell size
+ short words not more prominent
– small maximum size to accommodate long words

26. Inclusion of Zero-count Words
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+ provides context (what is not in document)
– more cluttered

27. 27 Case Studies

28. 28

29. 29

30. 30

31. 31

32. 2008 U.S. Presidential Debate
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33. Unexpected Uses
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 WordNet Visualization

34. Unexpected Uses
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 WordNet Visualization

35. Unexpected Uses
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 Language Education
 “invaluable potential for writing and vocabulary
development at the secondary level”
 “I'm very interested in using the program, I'm an English
teacher”

36. 36 Related Work

37. Types of Document
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Visualization

38. Features of Document
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Visualization
 Semantic: indicate meaning
 Cluster: generalize into concepts
 Overview: provide quick gist
 Zoom: support varying level of detail
 Compare: multi-document comparisons
 Search: find specific words/phrases
 Read: drill-down to original text
 Pattern: reveal patterns of repetition
 Features: reveal extracted features such as emotion
 Suggest: automatically select interesting focus words
 Phrases: can show multi-word phrases
 All words: can show all parts of speech

39. Features of Document
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Visualization

40. Semantics & Clustering
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 Provides word
definitions and
relations
 Clusters of
related terms
allow variable
level of
abstraction

41. Phrases & All Words
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 Cannot visualize multi-word phrases that are not
‘words’ in WordNet
 Only English nouns, verbs

42. 42 Future Work

43. Uneven Tree Cut Models
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44. 44

45. DocuBurst Comparative Views
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 Embed small multiples in e-libraries
 Colour scale based on text difference
 From each other
 From corpus average

47. Simplification
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 Root suggestion
 How to know where to start exploring?
 Word sense disambiguation
 Attempt to select a sense
 Use a less detailed ontology

48. Thanks for your Attention!
Acknowledgements:
Ravin Balakrishnan and helpful reviewers.
Contact: ccollins@cs.utoronto.ca
EuroVis 2009 Christopher Collins, Sheelagh Carpendale, and Gerald Penn