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P ASCAL C HALLENGE ON E VALUATING M ACHINE L EARNING FOR I NFORMATION E XTRACTION

P ASCAL C HALLENGE ON E VALUATING M ACHINE L EARNING FOR I NFORMATION E XTRACTION. Neil Ireson Local Challenge Coordinator Web Intelligent Group Department of Computer Science University of Sheffield UK. Organisers. Sheffield – Fabio Ciravegna UCD Dublin – Nicholas Kushmerick

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P ASCAL C HALLENGE ON E VALUATING M ACHINE L EARNING FOR I NFORMATION E XTRACTION

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  1. PASCAL CHALLENGE ON EVALUATING MACHINE LEARNING FOR INFORMATION EXTRACTION Neil Ireson Local Challenge Coordinator Web Intelligent Group Department of Computer Science University of Sheffield UK

  2. Organisers • Sheffield – Fabio Ciravegna • UCD Dublin – Nicholas Kushmerick • ITC-IRST – Alberto Lavelli • University of Illinois – Mary-Elaine Califf • FairIsaac – Dayne Freitag

  3. Outline • Challenge Goals • Data • Tasks • Participants • Experimental Results • Conclusions

  4. Goal : Provide a testbed for comparative evaluation of ML-based IE • Standardisation • Data • Partitioning • Same set of features • Corpus preprocessed using Gate • No features allowed other than the ones provided • Explicit Tasks • Evaluation Metrics • For future use • Available for further test with same or new systems • Possible to publish and new corpora or tasks

  5. Data (Workshop CFP) 2005 Testing Data 200 Workshop CFP 2000 Training Data 400 Workshop CFP 1993

  6. Set0 Set1 Set2 Set3 Data (Workshop CFP) 2005 Testing Data 200 Workshop CFP 2000 Training Data 400 Workshop CFP 1993

  7. 9 9 9 9 7 7 7 7 3 3 3 3 5 5 5 5 8 8 8 8 1 1 1 1 6 6 6 6 2 2 2 2 4 4 4 4 0 0 0 0 Set0 Set1 Set2 Set3 Data (Workshop CFP) 2005 Testing Data 200 Workshop CFP 2000 Training Data 400 Workshop CFP 1993

  8. Unannotated Data 1 250 Workshop CFP UnannotatedData 2 250 Conference CFP WWW Data (Workshop CFP) 2005 Testing Data 200 Workshop CFP 2000 Training Data 400 Workshop CFP 1993

  9. Annotation Slots

  10. Preprocessing • GATE • Tokenisation • Part-Of-Speech • Named-Entities • Date, Location, Person, Number, Money

  11. Evaluation Tasks • Task1 - ML for IE:Annotating implicit information • 4-fold cross-validation on 400 training documents • Final Test on 200 unseen test documents • Task2a - Learning Curve: • Effect of increasing amounts of training data on learning • Task2b - Active learning: Learning to select documents • Given seed documents select the documents to add to training set • Task3a – Semi-supervised Learning: Given data • Same as Task1 but can use the 500 unannotated documents • Task3b - Semi-supervised Learning: Any Data • Same as Task1 but can use all available unannotated documents

  12. Evaluation • Precision/Recall/F1Measure • MUC Scorer • Automatic Evaluation Server • Exact matching • Extract every slot occurrence

  13. Participants

  14. Task1 Information Extraction with all the available data

  15. Task1: Test Corpus

  16. Task1: Test Corpus

  17. Task1: Test Corpus

  18. Task1: 4-Fold Cross-validation

  19. Task1: 4-Fold & Test Corpus

  20. Task1: Slot FMeasure

  21. Best Slot FMeasures Task1: Test Corpus

  22. Task 2a Learning Curve

  23. Task2a: Learning Curve FMeasure

  24. Task2a: Learning Curve Precision

  25. Task2a: Learning Curve Recall

  26. Task 2b Active Learning

  27. Task2b: Active Learning • Amilcare • Maximum divergence from expected number of tags. • Hachey • Maximum divergence between two classifiers built on different feature sets. • Yaoyong (Gram-Schmidt) • Maximum divergence between example subset.

  28. Task2b: Active LearningIncreased FMeasure over random selection

  29. Task 3 Semi-supervised learning (not significant participation)

  30. Conclusions (Task1) • Top three (4) systems use different algorithms • Rule Induction, SVM, CRF & HMM • Same algorithms (SVM) produced different results • Brittle Performance • Large variation on slot performance • Post-processing

  31. Conclusion (Task2 & Task3) • Task 2a: Learning Curve • Systems’ performance is largely as expected • Task 2b: Active Learning • Two approaches, Amilcare and Hachey, showed benefits • Task 3: Semi-supervised Learning • Not sufficient participation to evaluate use of enrich data

  32. Future Work • Performance differences: • Systems: what determines good/bad performance • Slots: different systems were better/worse at identifying different slots • Combine approaches • Active Learning • Semi-supervised Learning • Overcoming the need for annotated data • Extensions • Data: Use different data sets and other features, using (HTML) structured data • Tasks: Relation extraction

  33. Thank You http://tyne.shef.ac.uk/Pascal

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