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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

INTRODUCTION TO ARTIFICIAL INTELLIGENCE. Massimo Poesio Supervised Relation Extraction. RE AS A CLASSIFICATION TASK. Binary relations Entities already manually/automatically recognized Examples are generated for all sentences with at least 2 entities

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INTRODUCTION TO ARTIFICIAL INTELLIGENCE

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  1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE Massimo PoesioSupervised Relation Extraction

  2. RE AS A CLASSIFICATION TASK • Binary relations • Entities already manually/automatically recognized • Examples are generated for all sentences with at least 2 entities • Number of examples generated per sentence isNC2 – Combination of N distinct entities selected 2 at a time

  3. GENERATING CANDIDATES TO CLASSIFY

  4. RE AS A BINARY CLASSIFICATION TASK

  5. NUMBER OF CANDIDATES TO CLASSIFY – SIMPLE MINDED VERSION

  6. THE SUPERVISED APPROACH TO RE • Most current approaches to RE are kernel-based • Different information is used • Sequences of words, e.g., through the GLOBAL CONTEXT / LOCAL CONTEXT kernels of Bunescu and Mooney / GiulianoLavelli & Romano • Syntactic information through the TREE KERNELS of Zelenko et al / Moschitti et al • Semantic information in recent work

  7. KERNEL METHODS: A REMINDER • Embedding the input data in a feature space • Using a linear algorithm for discovering non-linear patterns • Coordinates of images are not needed, only pairwise inner products • Pairwiseinner products can be efficiently computed directly from X using a kernel function K:X×X→R

  8. MODULARITY OF KERNEL METHODS

  9. THE WORD-SEQUENCE APPROACH • Shallow linguistic Information: • tokenization • Lemmatization • sentence splitting • PoStagging Claudio Giuliano, Alberto Lavelli, and Lorenza Romano (2007), FBK-IRST: Kernel methods for relation extraction, Proc. Of SEMEVAL-2007

  10. LINGUISTIC REALIZATION OF RELATIONS Bunescu & Mooney, NIPS 2005

  11. WORD-SEQUENCE KERNELS • Two families of “basic” kernels • Global Context • Local Context • Linear combination of kernels • Explicit computation • Extremely sparse input representation

  12. THE GLOBAL CONTEXT KERNEL

  13. THE GLOBAL CONTEXT KERNEL

  14. THE LOCAL CONTEXT KERNEL

  15. LOCAL CONTEXT KERNEL (2)

  16. KERNEL COMBINATION

  17. EXPERIMENTAL RESULTS • Biomedical data sets • AIMed • LLL • Newspaper articles • Roth and Yih • SEMEVAL 2007

  18. EVALUATION METHODOLOGIES

  19. EVALUATION (2)

  20. EVALUATION (3)

  21. EVALUATION (4)

  22. RESULTS ON AIMED

  23. OTHER APPROACHES TO RE • Using syntactic information • Using lexical features

  24. Syntactic information for RE • Pros: • more structured information useful when dealing with long-distance relations • Cons: • not always robust • (and not available for all languages)

  25. Zelenko et al JMLR 2003 • TREE KERNEL defined over a shallow parse tree representation of the sentences • approach vulnerable to unrecoverable parsing errors • data set: 200 news articles (not publicly available) • two types of relations : person-affiliation and organization-location

  26. ZELENKO ET AL

  27. CULOTTA & SORENSEN 2004 • generalized version of Zelenko’s kernel based on dependency trees (smallest dependency tree containing the two entities of the relation) • a bag-of-words kernel is used to compensate syntactic errors • data set: ACE 2002 & 2003 • results: syntactic information improves performance w.r.t. bag-of-words (good precision but low recall)

  28. CULOTTA AND SORENSEN (2)

  29. EVALUATION CAMPAIGNS FOR RE • Much of modern evaluation of methods is done by competing with other teams on evaluation campaigns like MUC and ACE • Modern evaluation campaigns for RE: SEMEVAL (now *SEM) • Interesting to look also at the problems of • DATA CREATION • EVALUATION METRICS

  30. SEMEVAL 2007 • 4th International Workshop on Semantic Evaluations • Task 04: Classification of Semantic Relations between Nominals • organizers: Roxana Girju, Marti Hearst, PreslavNakov, ViviNastase, Stan Szpakowicz, Peter Turney, DenizYuret • 14 participating teams

  31. SEMEVAL 2007: THE RELATIONS

  32. SEMEVAL 2007: DATASET CREATION

  33. SEMEVAL 2007: DATASET CREATION (2)

  34. SEMEVAL 2007 – DATASET CREATION (3)

  35. SEMEVAL 2007 – DATASET CREATION (4)

  36. SEMEVAL 2007: DATASET

  37. SEMEVAL 2007: COMPETITION

  38. SEMEVAL 2007: COMPETITION (2)

  39. SEMEVAL 2007: BEST RESULTS

  40. INFLUENCE OF NER ON RE

  41. INFLUENCE OF NER ON RE (2)

  42. GENERATING CANDIDATES

  43. GENERATING CANDIDATES

  44. GENERATING CANDIDATES

  45. ACKNOWLEDGMENTS • Many slides borrowed from • Roxana Girju • Alberto Lavelli

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