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Understanding Text Corpora with Multiple Facets Lei Shi, Furu Wei, Shixia Liu, Xiaoxiao Lian, Li Tan...
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Transcript of Understanding Text Corpora with Multiple Facets Lei Shi, Furu Wei, Shixia Liu, Xiaoxiao Lian, Li Tan...
Understanding Text Corpora with Multiple Facets
Lei Shi, Furu Wei, Shixia Liu, Xiaoxiao Lian, Li Tan and Michelle X. Zhou
IBM Research
Outline Problem & Related Work Multi-Facet Text Data Model and Text Processing
– Data model
– Text pre-processing
– Content summarization
Visualization– Metaphor
– Creation algorithm
– Interactions
Video Demo
Problem & Related Work It’s challenging to build a visual analytics
tool to explain multi-faceted text corpora!– How to combine the raw text data with rich
text analytics result for visualization?
– What visual metaphors to apply to effectively illustrate text content, evolution and facet correlations?
– How to customize interactions to assist user in data navigation and other visual analytics task?
Related work– Text trend visualization
• ThemeRiver, NameVoyager, etc.
– Text content visualization• Tag cloud, Wordle, PhraseNet, etc.
– Text entity pattern visualization• TileBars, Jigsaw, FeatureLens, Takmi, etc.
– Text visualization in specific domains• Themail@email, TileBars@search,
Multi-Facet Data Model and Text Pre-Processing
Multi-Facet Data Model for Text Corpora -- – Time Facet
• Explicit field or extracted from raw text
– Category Facet• Topic modeling by Latent Dirichlet Allocation (LDA, Blei et al. 2003)• Category labels from document classification/clustering• Leverage other nominal structured information (hotel names, countries, etc.)
– Unstructured (Content) Facets• Inherent multiple text fields• Multiple facets from NE extraction (people, location, organization) or POS
parsing (Noun, Verbs, Adjective)
– Structured Facets• Categorical, numerical or nominal data fields• Other calculated categorical value (sentiment orientations, average ratings)
TFFD su ,,,
Content Facet Summarization
A set of topics {T1, …Ti,… TN }
A set of keywords
{W1, …, Wj, …, WM}
A set of topic probabilities
{…, P(Ti | Dk), …}
A set of word probabilities
{…, P(Wj | Ti), …}
kth document in the collection
Rank the topics to present most valuable ones first
Select keyword sub-set for each time segment for content summary
{…} t-1, {…, Wj, …}t, {…} t+1,
Doc-topic dist.
Doc lengthDoc no.
Content Facet Summarization Topic/category re-ranking by topic coverage and variance: find the most
active topic with significant variety– Topic coverage:
– Topic variance:
– Balancing two metrics:
Keyword re-ranking– Topic keyword re-ranking:
– Time-sensitive keyword re-ranking: preserve completeness and distinctiveness
• Completeness: cover the original keywords of a topic
• Distinctiveness: distinguish one time segment from another
Topic-keyword distribution
Topic number
System Architecture
Text Summarization
Text Preprocessing
Text content + meta data
Visualization
Text collection
UserInteraction
Summarizationresults
Visualization Metaphors Multi-stack trend visualization + Time-sensitive tag clouds
– Vis-data mappings: time facet – x (time) axis, category facet – stack, unstructured facets – tag clouds, structured facet – keyword style (color/font)
– Other mappings: document count – y axis, re-ranked occurrence count -- keyword size
Category Facet
Time
Unstructured Facets
Structured Facets
Interactions Temporal zooming for time facet navigation Topic editing for category facet navigation Unstructured facet navigation panel Structured facet mapping Other customized interactions: topic focus-in-context view
Focus-In-Context View Calculation Constraints for detailed trend view
– Contour-preserving
– Flexible space control
– All topic trends as undistorted as possible
1D fisheye distortion– Height calculation for expanded trend
– Order-preserving height adjustment
– Apply fisheye distortion from the center line of selected topic