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Modeling Text and Links: Overview

Modeling Text and Links: Overview. William W. Cohen Machine Learning Dept. and Language Technologies Institute School of Computer Science Carnegie Mellon University

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Modeling Text and Links: Overview

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  1. Modeling Text and Links: Overview William W. Cohen Machine Learning Dept. and Language Technologies Institute School of Computer Science Carnegie Mellon University Joint work with: Amr Ahmed, Andrew Arnold, Ramnath Balasubramanyan, Frank Lin, Matt Hurst (MSFT), Ramesh Nallapati, Noah Smith, Eric Xing, Tae Yano

  2. Outline • Tools for analysis of text • Probabilistic models for text, communities, and time • Mixture models and LDA models for text • LDA extensions to model hyperlink structure

  3. Introduction to Topic Models

  4. Introduction to Topic Models • Probabilistic Latent Semantic Analysis (PLSA) d d • Select document d ~ Mult() • For each position n = 1,, Nd • generate zn ~ Mult( z| d) • generate wn ~ Mult( z| zn)  Doc-specific z Topic distribution • Mixture model: • each document is generated by a single (unknown) multinomial distribution of words, the corpus is “mixed” by  • PLSA model: • each word is generated by a single unknown multinomial distribution of words, each document is mixed by d w N M 

  5. Introduction to Topic Models • PLSA topics (TDT-1 corpus)

  6. Introduction to Topic Models JMLR, 2003

  7. Introduction to Topic Models • Latent Dirichlet Allocation  • For each document d = 1,,M • Generate d ~ Dir(¢ | ) • For each position n = 1,, Nd • generate zn ~ Mult( z| d) • generate wn ~ Mult( w| zn) a z w N M 

  8. Introduction to Topic Models • Perplexity comparison of various models Unigram Mixture model PLSA Lower is better LDA

  9. Introduction to Topic Models • Prediction accuracy for classification using learning with topic-models as features Higher is better

  10. Outline • Tools for analysis of text • Probabilistic models for text, communities, and time • Mixture models and LDA models for text • LDA extensions to model hyperlink structure • LDA extensions to model time • Alternative framework based on graph analysis to model time & community • Preliminary results & tradeoffs • Discussion of results & challenges

  11. Hyperlink modeling using PLSA

  12. Hyperlink modeling using PLSA[Cohn and Hoffman, NIPS, 2001]  • Select document d ~ Mult() • For each position n = 1,, Nd • generate zn ~ Mult( ¢ | d) • generate wn ~ Mult( ¢ | zn) • For each citation j = 1,, Ld • generate zj ~ Mult( ¢ | d) • generate cj ~ Mult( ¢ | zj) d d z z w c N L M  g

  13. Hyperlink modeling using PLSA[Cohn and Hoffman, NIPS, 2001]  PLSA likelihood: d d z z New likelihood: w c N L M  g Learning using EM

  14. Hyperlink modeling using PLSA[Cohn and Hoffman, NIPS, 2001] Heuristic:  (1-) 0 ·· 1 determines the relative importance of content and hyperlinks

  15. Hyperlink modeling using PLSA[Cohn and Hoffman, NIPS, 2001] • Experiments: Text Classification • Datasets: • Web KB • 6000 CS dept web pages with hyperlinks • 6 Classes: faculty, course, student, staff, etc. • Cora • 2000 Machine learning abstracts with citations • 7 classes: sub-areas of machine learning • Methodology: • Learn the model on complete data and obtain d for each document • Test documents classified into the label of the nearest neighbor in training set • Distance measured as cosine similarity in the  space • Measure the performance as a function of 

  16. Hyperlink modeling using PLSA[Cohn and Hoffman, NIPS, 2001] • Classification performance content Hyperlink Hyperlink content

  17. Hyperlink modeling using LDA

  18. Hyperlink modeling using LinkLDA[Erosheva, Fienberg, Lafferty, PNAS, 2004] a  • For each document d = 1,,M • Generate d ~ Dir(¢ | ) • For each position n = 1,, Nd • generate zn ~ Mult( ¢ | d) • generate wn ~ Mult( ¢ | zn) • For each citation j = 1,, Ld • generate zj ~ Mult( . | d) • generate cj ~ Mult( . | zj) z z w c N L M  g Learning using variational EM

  19. Hyperlink modeling using LDA[Erosheva, Fienberg, Lafferty, PNAS, 2004]

  20. Goals of analysis: Extract information about events from text “Understanding” text requires understanding “typical reader” conventions for communicating with him/her Prior knowledge, background, … Goals of analysis: Very diverse Evaluation is difficult And requires revisiting often as goals evolve Often “understanding” social text requires understanding a community NewswireText Social MediaText Science as a testbed for social text: an open community which we understand

  21. Author-Topic Model for Scientific Literature

  22. Author-Topic Model for Scientific Literature[Rozen-Zvi, Griffiths, Steyvers, Smyth UAI, 2004] a P • For each author a = 1,,A • Generate a ~ Dir(¢ | ) • For each topic k = 1,,K • Generate fk ~ Dir( ¢ | ) • For each document d = 1,,M • For each position n = 1,, Nd • Generate author x ~ Unif(¢ | ad) • generate zn ~ Mult( ¢ | a) • generate wn ~ Mult( ¢ | fzn) a x z  A w N M f b

  23. Author-Topic Model for Scientific Literature[Rozen-Zvi, Griffiths, Steyvers, Smyth UAI, 2004] a Learning: Gibbs sampling P  x z  A w N M f b

  24. Author-Topic Model for Scientific Literature[Rozen-Zvi, Griffiths, Steyvers, Smyth UAI, 2004] • Topic-Author visualization

  25. Stopped here

  26. Author-Topic Model for Scientific Literature[Rozen-Zvi, Griffiths, Steyvers, Smyth UAI, 2004] • Application 1: Author similarity

  27. Author-Topic-Recipient model for email data [McCallum, Corrada-Emmanuel,Wang, ICJAI’05]

  28. Author-Topic-Recipient model for email data [McCallum, Corrada-Emmanuel,Wang, ICJAI’05] Gibbs sampling

  29. Author-Topic-Recipient model for email data [McCallum, Corrada-Emmanuel,Wang, ICJAI’05] • Datasets • Enron email data • 23,488 messages between 147 users • McCallum’s personal email • 23,488(?) messages with 128 authors

  30. Author-Topic-Recipient model for email data [McCallum, Corrada-Emmanuel,Wang, ICJAI’05] • Topic Visualization: Enron set

  31. Author-Topic-Recipient model for email data [McCallum, Corrada-Emmanuel,Wang, ICJAI’05] • Topic Visualization: McCallum’s data

  32. Modeling Citation Influences

  33. Modeling Citation Influences[Dietz, Bickel, Scheffer, ICML 2007] • Copycat model of citation influence • LDA model for cited papers • Extended LDA model for citing papers • For each word, depending on coin flip c, you might chose to copy a word from a cited paper instead of generating the word

  34. Modeling Citation Influences[Dietz, Bickel, Scheffer, ICML 2007] • Citation influence model

  35. Modeling Citation Influences[Dietz, Bickel, Scheffer, ICML 2007] • Citation influence graph for LDA paper

  36. Amr Ahmed Eric Xing Models of hypertext for blogs [ICWSM 2008] Ramesh Nallapati me

  37. LinkLDA model for citing documents Variant of PLSA model for cited documents Topics are shared between citing, cited Links depend on topics in two documents Link-PLSA-LDA

  38. Experiments • 8.4M blog postings in Nielsen/Buzzmetrics corpus • Collected over three weeks summer 2005 • Selected all postings with >=2 inlinks or >=2 outlinks • 2248 citing (2+ outlinks), 1777 cited documents (2+ inlinks) • Only 68 in both sets, which are duplicated • Fit model using variational EM

  39. Topics in blogs Model can answer questions like: which blogs are most likely to be cited when discussing topic z?

  40. Topics in blogs Model can be evaluated by predicting which links an author will include in a an article Link-LDA Link-PLDA-LDA Lower is better

  41. a   z  z z z w c w N N  Another model: Pairwise Link-LDA • LDA for both cited and citing documents • Generate an indicator for every pair of docs • Vs. generating pairs of docs • Link depends on the mixing components (’s) • stochastic block model

  42. Pairwise Link-LDA supports new inferences… …but doesn’t perform better on link prediction

  43. Outline • Tools for analysis of text • Probabilistic models for text, communities, and time • Mixture models and LDA models for text • LDA extensions to model hyperlink structure • Observation: these models can be used for many purposes… • LDA extensions to model time • Alternative framework based on graph analysis to model time & community • Discussion of results & challenges

  44. Authors are using a number of clever tricks for inference….

  45. Predicting Response to Political Blog Posts with Topic Models [NAACL ’09] Noah Smith Tae Yano

  46. Political blogs and and comments Posts are often coupled with commentsections Comment style is casual, creative, less carefully edited 50

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