This document presents DocuBurst, a visualization tool that analyzes document content using the semantic relationships between words in WordNet. DocuBurst displays words from documents in a node-link diagram based on the WordNet ontology, with node size and color encoding word frequency and sense. This semantic structure enables multi-level abstraction and comparison of documents. The tool supports interactions like searching, filtering, zooming and exploring word relationships. Evaluation shows DocuBurst is useful for tasks like content analysis, plagiarism detection and language education.

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
5
 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
6
 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
7
 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
8
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
22

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
32

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
37
Visualization

38. Features of Document
38
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
39
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
41
 Cannot visualize multi-word phrases that are not
‘words’ in WordNet
 Only English nouns, verbs

42. 42 Future Work

43. Uneven Tree Cut Models
43

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
47
 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