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Topic Significance Ranking for LDA Generative Models

Topic Significance Ranking for LDA Generative Models. Loulwah AlSumait Daniel Barbará James Gentle Carlotta Domeniconi. ECML PKDD - Bled, Slovenia - September 7-11, 2009. Agenda. Introduction Junk/Insignificant topic definitions Distance measures

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Topic Significance Ranking for LDA Generative Models

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  1. Topic Significance Ranking for LDA Generative Models Loulwah AlSumait Daniel Barbará James Gentle Carlotta Domeniconi ECML PKDD - Bled, Slovenia - September 7-11, 2009

  2. Agenda • Introduction • Junk/Insignificant topic definitions • Distance measures • 4-phase Weighted Combination Approach • Experimental results • Conclusions and future work

  3. d zi  Nd D Latent Dirichlet Allocation (LDA) ModelBlei, Ng, & Jordan (2003) • Exact inference is intractable • Approximation approaches • Input: K • Output: Φ, θ • Probabilistic generative model • Hidden variables (topics) are associated with the observed text • Dirichlet priors on document and topic distributions  Inference Process Generative Process K wi

  4. Topic Significance Ranking • Critical effect of the setting of K on the inferred topics • Most of previous work manually examine the topics • Quantify the semantic significance of topics • How much different is the topic distribution from junk/insignificant topic distributions

  5. Topic Significance Ranking • Example: 20 NewsGroup The Volgenau School of Information Technology and Engineering Department of Computer Science

  6. Junk/Insignificant Topic Definitions • Uniform Distribution Over Words • Uniformity of a topic: • Vacuous Semantic Distribution • , p(wi|k) = ik , • Vacuousness of a topic: • Background Distribution • Background of a topic: ,

  7. Distance Measures • Symmetric KL-Divergence • Uniformity, Background, W-Vacuous • Cosine Dissimilarity • Uniformity , W-Vacuous , Background • Coefficient Correlation • Uniformity , W-Vacuous , Background

  8. Topic Significance Ranking • Multi-Criteria Weighted Combination • 4 phases • Standardization procedure • Transfer distances into standardized measures • Scores • Weights

  9. B U V B V U S S S S S S 1 1 1 2 2 2 k k k k k k W-Vacuous scores Background scores Topic Significance Ranking • 4 phases (Continued) • Intra-Criterion Weighted Combination • Combine standardized measures of each J/I definition • Inter-Criteria Weighted Combination • Combine J/I scores and weights • Topic Rank Uniformity scores TSR X

  10. Experimental Results: Simulated Data

  11. 20NewsGroupsTop 10 significant topics

  12. 20NewsGroupsLowest 10 significant topics

  13. NIPSTop 10 Significant Topics

  14. NIPSLowest 10 Significant Topics

  15. Individual vs. Combined Score Simulated Data

  16. Individual vs. Combined Score 20 NewsGroups

  17. Conclusions and Future Work • Unsupervised numerical quantification of the topics’ semantic Significance • Novel post analysis in LDA modeling • Three J/I topic distributions • 4 levels of weighted combination approach • Future directions: • Analysis of TSR sensitivity to the approach, K and weights settings • More J/I definitions • Tool to visualize topic evolution in online setting

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