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From bursty patterns to bursty facts: The effectiveness of temporal text mining for news

From bursty patterns to bursty facts: The effectiveness of temporal text mining for news. Ilija Subašić & Bettina Berendt K.U. Leuven, Belgium. Agenda. The first problem: temporal text mining Solution methods The second problem: evaluation of 2) Our approach: cross-evaluation framework

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From bursty patterns to bursty facts: The effectiveness of temporal text mining for news

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  1. From bursty patterns to bursty facts:The effectiveness of temporal text mining for news Ilija Subašić & Bettina Berendt K.U. Leuven, Belgium

  2. Agenda • The first problem: temporal text mining • Solution methods • The second problem: evaluation of 2) • Our approach: cross-evaluation framework • Case study: evaluation of 3 methods

  3. Temporal text mining (TTM): The problem time • What happened? • What were the important new developments in a time period?

  4. TTM: keyword representation methods [Kleinberg, 2002] “bursty“ ~ more frequent in 1985-94 than in the whole analysed time

  5. TTM:group representation methods [Mei & Zhai, 2005]

  6. TTM: combo representation methods– STORIES: graphical summary [Subašić, Berendt, 2008+]

  7. Demo at ECML/PKDD STORIES: story tracking and exploration

  8. Evaluations so far • Standardized tasks and competitions • DUC update task & ROUGE framework: summarization • TREC Novelty track (2002-04): novel-sentence retrieval • Disadvantages: • # documents too small: 10 for DUC and 25 per topic for TREC • Output is textual  not possible to compare all TTM methods • TTM: evaluations limited to the respective method and corpora

  9. Retrieved sentences Patterns Queries Cross-evaluation: Our approach (1) Sentence retrieval Query-likelihood retrieval QL More : https://sites.google.com/site/subasicilija/ttm-evaluation Query generation Generic / method-specific (top bursty elements & combinations) shave hair britney_spears Last Friday, pop star Britney shaved her head, parting with her long hair.

  10. Our approach (2):Sentences‘ “precision/recall“ Sentence retrieval Query-likelihood retrieval QL Query generation Generic / method-specific (top bursty elements & combinations) Retrieved sentences Patterns Queries IR-style evaluation ROUGE2, ROUGE.SU4, aggregate measures, Friedman and Tukey‘s multiple comparison test Ground-truth sentences

  11. Our approach (3):“Recall-oriented“ aggregate measure maxMR Retrieved sentences t Ground- truth sentences t Best fit (ROUGE) Normalize by max. possible best fit All sentences t

  12. Our approach (4):“Precision-oriented“ aggregate measure maxMP Retrieved sentences method I Ground- truth sentences  Method II has a better chance of good matches  Scale maxMR by Retrieved sentences method II

  13. Case study experiment: Data & settings • Corpus 1: Crime case • 21 weeks, 306 documents, 31 ground-truth sentences • Corpus 2: Celebrity reporting • 8 weeks, 3000 documents, 19 ground-truth sentences • Corpora available at https://sites.google.com/site/subasicilija/ttm-evaluation • M1: a keyword representation method: Kleinberg‘s bursty words • M2: a group repr.method: Mei & Zhai‘s temporal text mining • M3: a combo representation method: STORIES

  14. Results: Top group method comparison maxMR maxMP

  15. Results: Top group method comparison maxMR maxMP

  16. maxMP maxMR Results: Query generation comparison

  17. maxMP maxMR Results: Query generation comparison

  18. Summary • First cross-methods evaluation framework for Temporal Text Mining methods with different patterns • Experimental investigation of 3 TTM types • Results: • different methods – different strengths and weaknesses • M3/named entities: most robust method over settings • M3 variants > M1, M2 in “precision-oriented” measures • specific query generation improves “precision-oriented” results, especially for M1 and M2 • corpus dependence

  19. Future work • Standardized, bigger, more varied datasets • Establish a baseline (ROUGE originally for longer text sequences) • Explore possible sources of bias for/against specific methods • User studies (in progress)

  20. STORIES: graphical summary, textual summary, documents

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