1. Learning Ensembles of First-Order Clauses for Recall-Precision Curves A Case Study in Biomedical Information Extraction Mark Goadrich, Louis Oliphant and Jude Shavlik Department of Computer Sciences University of Wisconsin – Madison USA 6 Sept 2004

2. Talk Outline Link Learning and ILP Our Gleaner Approach Aleph Ensembles Biomedical Information Extraction Evaluation and Results Future Work

3. ILP Domains Object Learning Trains, Carcinogenesis Link Learning Learning binary predicates

4. Link Learning Large skew toward negatives 500 relational objects 5000 positive links means 245,000 negative links Difficult to measure success Always negative classifier is 98% accurate ROC curves look overly optimistic Enormous quantity of data 4,285,199,774 web pages indexed by Google PubMed includes over 15 million citations

5. Our Approach Develop fast ensemble algorithms focused on recall and precision evaluation Key Ideas of Gleaner Keep wide range of clauses Create separate theories for different recall ranges Evaluation Area Under Recall Precision Curve (AURPC) Time = Number of clauses considered

6. Gleaner - Background Focus evaluation on positive examples Recall = Precision = Rapid Random Restart ( Zelezny et al ILP 2002 ) Stochastic selection of starting clause Time-limited local heuristic search We store variety of clauses (based on recall) FNTP  FPTP 

7. Gleaner - Learning Precision Recall Create B Bins Generate Clauses Record Best Repeat for K seeds

8. Gleaner - Combining Combine K clauses per bin If at least L of K clauses match, call example positive How to choose L ? L=1 then high recall, low precision L=K then low recall, high precision Our method Choose L such that ensemble recall matches bin b Bin b’s precision should be higher than any clause in it We should now have set of high precision rule sets spanning space of recall levels

9. How to use Gleaner Precision Recall Generate Curve User Selects Recall Bin Return Classifications With Precision Confidence Recall = 0.50 Precision = 0.70

10. Aleph Ensembles We compare to ensembles of theories Algorithm ( Dutra et al ILP 2002 ) Use K different initial seeds Learn K theories containing C clauses Rank examples by the number of theories Need to balance C for high performance Small C leads to low recall Large C leads to converging theories

11. Aleph Ensembles (100 theories)

12. Biomedical Information Extraction Given: Medical Journal abstracts tagged with protein localization relations Do: Construct system to extract protein localization phrases from unseen text NPL3 encodes a nuclear protein with an RNA recognition motif and similarities to a family of proteins involved in RNA metabolism.

13. Biomedical Information Extraction Hand-labeled dataset (Ray & Craven ’01) 7,245 sentences from 871 abstracts Examples are phrase-phrase combinations 1,810 positive & 279,154 negative 1.6 GB of background knowledge Structural, Statistical, Lexical and Ontological In total, 200+ distinct background predicates More info on dataset by Jude Shavlik later

14. Evaluation Metrics Two dimensions Area Under Recall- Precision Curve (AURPC) All curves standardized to cover full recall range Averaged AURPC over 5 folds Number of clauses considered Rough estimate of time Both are “stop anytime” parallel algorithms Recall Precision 1.0

15. AURPC Interpolation Convex interpolation in RP space? Precision interpolation is counterintuitive Example: 1000 positive & 9000 negative TPFPTP RateFP RateRecallPrec 500 0.500.060.50 100090001.00 0.10 Example CountsRP CurvesROC Curves 75047500.750.530.750.14

16. AURPC Interpolation

17. Experimental Methodology Performed five-fold cross-validation Variation of parameters Gleaner (20 recall bins) # seeds = {25, 50, 75, 100} # clauses = {1K, 10K, 25K, 50K, 100K, 250K, 500K} Ensembles (0.75 minacc, 35,000 nodes) # theories = {10, 25, 50, 75, 100} # clauses per theory = {1, 5, 10, 15, 20, 25, 50}

18. Results: Testfold 5 at 1,000,000 clauses Ensembles Gleaner

19. Results: Gleaner vs Aleph Ensembles

20. Conclusions Gleaner Focuses on recall and precision Keeps wide spectrum of clauses Good results in few cpu cycles Aleph ensembles ‘Early stopping’ helpful Require more cpu cycles AURPC Useful metric for comparison Interpolation unintuitive

21. Future Work Improve Gleaner performance over time Explore alternate clause combinations Better understanding of AURPC Search for clauses that optimize AURPC Examine more ILP link-learning datasets Use Gleaner with other ML algorithms

22. Take-Home Message Definition of Gleaner One who gathers grain left behind by reapers Gleaner and ILP Many clauses constructed and evaluated in ILP hypothesis search We need to make better use of those that aren’t the highest scoring ones Thanks, Questions?

23. Acknowledgements USA NLM Grant 5T15LM007359-02 USA NLM Grant 1R01LM07050-01 USA DARPA Grant F30602-01-2-0571 USA Air Force Grant F30602-01-2-0571 Condor Group David Page Vitor Santos Costa, Ines Dutra Soumya Ray, Marios Skounakis, Mark Craven Dataset available at (URL in proceedings) ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/datasets/IE-protein-location

24. Deleted Scenes Aleph Learning Clause Weighting Sample Gleaner Recall-Precision Curve Sample Extraction Clause Gleaner Algorithm Director Commentary onoff

25. Aleph - Learning Aleph learns theories of clauses (Srinivasan, v4, 2003) Pick a positive seed example and saturate Use heuristic search to find best clause Pick new seed from uncovered positives and repeat until threshold of positives covered Theory produces one recall-precision point Learning complete theories is time-consuming Can produce ranking with theory ensembles

26. Clause Weighting Single Theory Ensemble rank by how many clauses cover examples Weight clauses using tuneset statistics CN2 (average precision of matching clauses) Lowest False Positive Rate Score Cumulative F1 score Recall Precision Diversity

27. Clause Weighting

28. Biomedical Information Extraction NPL3 encodes a nuclear protein with … verbnounarticleadjnounprepsentence prep phrase … verb phrase noun phrase noun phrase alphanumeric marked location

29. Sample Extraction Clause P = Protein, L = Location, S = Sentence 29% Recall 34% Precision on testset 1 S CB A article contains alphanumeric contains alphanumeric P noun L noun contains marked location contains no between halfX verb

30. Gleaner Algorithm Create B equal-sized recall bins For K different seeds Generate rules using Rapid Random Restart Record best rule (precision x recall) found for each bin For each recall bin B Find threshold L of K clauses such that recall of “at least L of K clauses match examples” = recall for this bin Find recall and precision on testset using each bin’s “at least L of K” decision process

31. Further Results