4. Relationship Extraction

1. 4. Relationship Extraction Part 4 of Information Extraction SunitaSarawagi CS 652, Peter Lindes

2. The Problem • Relate extracted entities – unstructured text not partitioned into records • Various competitions • MUC • ACE • BioCreAtIvE II Protein-Protein Interaction CS 652, Peter Lindes

3. Groups of Relationships • ACE: • located at, near, part, role, social for entities: • person, organization, facility, location, and geo-political entity • Biomedical: gene-disease, protein-protein, subcellular regularizations • NAGA knowledge base: 26 relationships such as: isA, bornInYear, establishedInYear, hasWonPrize, locatedIn, politicianOf, … CS 652, Peter Lindes

4. Three Problem Levels • First case: • Entities preidentified in unstructured text • Given a pair of entities, find type of relationship • Second case: • Given relationship type r, entity name e • Extract entities with which e has relationship r • Third case: • Open-ended corpus – the web • Given relationship type r, find entity pairs CS 652, Peter Lindes

5. Given Entity Pair, Find Relationship • R: set of relationship types • : R plus a special member for “other” • x: a “snippet” of text (might be a sentence) • E1 and E2 in x • Identify relationships in between E1 and E2 • Resources available: • Surface Tokens • Part of Speech tags • Syntactic Parse Tree Structure • Dependency Graph • Use these clues to classify (x, E1,E2) into one of CS 652, Peter Lindes

6. Parse Tree CS 652, Peter Lindes

7. Dependency Graph CS 652, Peter Lindes

8. Methods to Extract Relationships • Feature-based methods • String form, orthographic type, POS tag, etc. • Features from Dependency Graph • Features from Word Sequence • Features from Parse Trees • Kernel-based methods • Kernel function K(X, X’) captures similarity • Support Vector Machine (SVM) classifier • Rule-based methods CS 652, Peter Lindes

9. Given Relationship, Find Entity Pairs • Given one or more relationship types • Find all occurrences in a corpus • Open document collection • No labeled unstructured training data • Instead, seeding for each relationship type is used CS 652, Peter Lindes

10. Seed Data for Relationship Type r • The types of entities that are arguments of r • Often specified at a high level, eg. proper noun, common noun, numeric, etc. • Types such as “Person” or “Company” require patterns to recognize them • A seed database S of entities that have r • May include negative examples • A seed set or manually coded patterns • Easy for generic relationships, eg. hypernym or meronym (part-of) CS 652, Peter Lindes

11. 3 Steps for Relationship Extraction • Start with above seeding data • A corpus D • Relationship types r1,…,rk • Entity types Tr1, Tr2 for each r • A set S of examples (Ei1,Ei2,ri) 1 ≤ i ≤ N • 1: Use S to learn extraction patterns M • 2: Use a subset of patterns to create candidates • 3: Validation: select a subset based on statistical tests CS 652, Peter Lindes

12. Example Data • Relationships: “IsPhDAdvisorOf”, “Acquired” • Entity types: “(Person, Person)”, “(Company, Company)” CS 652, Peter Lindes

13. Learn Patterns from Seed Triples • Assume only one relationship for each pair • Thus each example for r is negative for r’ • 1: Find sentences with entity pairs • For (E1,E2,r) query for “E1 NEAR E2” • Filter out where E1, E2 don’t match Tr1, Tr2 • 2: Filter sentences for the relationship • 3: Learn patterns from sentences CS 652, Peter Lindes

14. Filtering Sentences • Example: • Banko: a simple heuristic using the length of dependency links • This fails for above example CS 652, Peter Lindes

15. Learn Patterns from Sentences • Formulate as a standard classification problem • Two practical problems: • No guarantee of positive examples • Bunescu and Mooney: use SVM • Many sentences for each pair • Bunescu and Mooney: down-weight correlated terms CS 652, Peter Lindes

16. Extract Candidate Entity Pairs • Learned model M: (x,E1,E2) -> r • Simple method: sequential scan over D • Look for Tr1, Tr2, then apply M • Large, indexed corpus: retrieve relevant sentences • Use keyword search • Pattern-based • Keyword-based • Agichtein and Gravano: iterative solution CS 652, Peter Lindes

17. Validate Extracted Relationships • Extraction has high error rates • Validation based on corpus-wide statistics • Probabilities based on count of occurrences • Extract only high-confidence relationships • Rare relationships: • Use contextual pattern • Alternative: correct entity boundary errors CS 652, Peter Lindes

18. Summary • Setting 1: entities already marked • Feature-based and kernel-based methods • Clues from word sequence, parse trees, and dependency graphs • Training data with labeled relationships • Setting 2: open corpus, given relationship types • No labeled unstructured data • Seed database of (E1,E2,r) examples • Bootstrapping from seed data • Filter based on relevancy • Accuracy: • 50%-70% for closed benchmark datasets • Lots of special case handling for the web CS 652, Peter Lindes

19. Further Readings • Concentrated here on binary relationships • Natural extension: records with multi-way relationships • Requires cross-sentence analysis: • Co-reference resolution • Discourse analysis • Much literature on this topic • Future research: discovering relevant relationship types CS 652, Peter Lindes