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Features & Unification

Features & Unification. Ling 571 Deep Processing Techniques for NLP January 31, 2011. Roadmap. Features: Motivation Constraint & compactness Features Definitions & representations Unification Application of features in the grammar Agreement, subcategorization

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Features & Unification

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  1. Features & Unification Ling 571 Deep Processing Techniques for NLP January 31, 2011

  2. Roadmap • Features: Motivation • Constraint & compactness • Features • Definitions & representations • Unification • Application of features in the grammar • Agreement, subcategorization • Parsing with features & unification • Augmenting the Earley parser, unification parsing • Extensions: Types, inheritance, etc • Conclusion

  3. Constraints & Compactness • Constraints in grammar • S -> NP VP • They run. • He runs.

  4. Constraints & Compactness • Constraints in grammar • S -> NP VP • They run. • He runs. • But… • *They runs • *He run • *He disappeared the flight

  5. Constraints & Compactness • Constraints in grammar • S -> NP VP • They run. • He runs. • But… • *They runs • *He run • *He disappeared the flight • NP -> Det Nom • This flight

  6. Constraints & Compactness • Constraints in grammar • S -> NP VP • They run. • He runs. • But… • *They runs • *He run • *He disappeared the flight • NP -> Det Nom • This flight • These flights

  7. Constraints & Compactness • Constraints in grammar • S -> NP VP • They run. • He runs. • But… • *They runs • *He run • *He disappeared the flight • NP -> Det Nom • This flight • These flights • *This flights

  8. Constraints & Compactness • Constraints in grammar • S -> NP VP • They run. • He runs. • But… • *They runs • *He run • *He disappeared the flight • NP -> Det Nom • This flight • These flights • *This flights • Violate agreement (number), subcategorization

  9. Enforcing Constraints • Enforcing constraints

  10. Enforcing Constraints • Enforcing constraints • Add categories, rules

  11. Enforcing Constraints • Enforcing constraints • Add categories, rules • Agreement: • S-> NPsg3p VPsg3p, • S-> NPpl3p VPpl3p,

  12. Enforcing Constraints • Enforcing constraints • Add categories, rules • Agreement: • S-> NPsg3p VPsg3p, • S-> NPpl3p VPpl3p, • Subcategorization: • VP-> Vtrans NP, • VP -> Vintrans, • VP->Vditrans NP NP

  13. Enforcing Constraints • Enforcing constraints • Add categories, rules • Agreement: • S-> NPsg3p VPsg3p, • S-> NPpl3p VPpl3p, • Subcategorization: • VP-> Vtrans NP, • VP -> Vintrans, • VP->Vditrans NP NP • Explosive!, loses key generalizations

  14. Features • person: 1st, 2nd, 3rd • I, we; you; he, she, they • am, are, is

  15. Features • person: 1st, 2nd, 3rd • I, we; you; he, she, they • am, are, is • number: sg, pl • I am; we are

  16. Features • person: 1st, 2nd, 3rd • I, we; you; he, she, they • am, are, is • number: sg, pl • I am; we are • case: nom, acc • I, he; me, him • gender: masc, fem, neut • animacy: +/1 • etc

  17. Features • person: 1st, 2nd, 3rd • I, we; you; he, she, they • am, are, is • number: sg, pl • I am; we are • case: nom, acc • I, he; me, him

  18. Features • person: 1st, 2nd, 3rd • I, we; you; he, she, they • am, are, is • number: sg, pl • I am; we are • case: nom, acc • I, he; me, him • gender: masc, fem, neut

  19. Features • person: 1st, 2nd, 3rd • I, we; you; he, she, they • am, are, is • number: sg, pl • I am; we are • case: nom, acc • I, he; me, him • gender: masc, fem, neut • animacy: +/1 • etc

  20. Why features? • Need compact, general constraints • S -> NP VP

  21. Why features? • Need compact, general constraints • S -> NP VP • Only if NP and VP agree

  22. Why features? • Need compact, general constraints • S -> NP VP • Only if NP and VP agree • How can we describe agreement, subcat?

  23. Why features? • Need compact, general constraints • S -> NP VP • Only if NP and VP agree • How can we describe agreement, subcat? • Decompose into elementary features that must be consistent • E.g. Agreement

  24. Why features? • Need compact, general constraints • S -> NP VP • Only if NP and VP agree • How can we describe agreement, subcat? • Decompose into elementary features that must be consistent • E.g. Agreement • Number, person, gender, etc

  25. Why features? • Need compact, general constraints • S -> NP VP • Only if NP and VP agree • How can we describe agreement, subcat? • Decompose into elementary features that must be consistent • E.g. Agreement • Number, person, gender, etc • Augment CF rules with feature constraints • Develop mechanism to enforce consistency • Elegant, compact, rich representation

  26. Feature Representations • Fundamentally, Attribute-Value pairs • Values may be symbols or feature structures • Feature path: list of features in structure to value • “Reentrant feature structures”: share some struct • Represented as • Attribute-value matrix (AVM), or • Directed acyclic graph (DAG)

  27. Feature Representations • Fundamentally, Attribute-Value pairs • Features: atomic symbols from a finite set

  28. Feature Representations • Fundamentally, Attribute-Value pairs • Features: atomic symbols from a finite set • Values may be • Atomic symbols from a finite set Attribute-value matrix (AVM)

  29. Feature Representations • Fundamentally, Attribute-Value pairs • Features: atomic symbols from a finite set • Values may be • Atomic symbols from a finite set Attribute-value matrix (AVM) NUMBER PL

  30. Feature Representations • Fundamentally, Attribute-Value pairs • Features: atomic symbols from a finite set • Values may be • Atomic symbols from a finite set Attribute-value matrix (AVM) NUMBER PL PERSON 3

  31. Feature Representations • Fundamentally, Attribute-Value pairs • Features: atomic symbols from a finite set • Values may be • Atomic symbols from a finite set Attribute-value matrix (AVM) NUMBER PL PERSON 3 NUMBER PL PERSON 3

  32. Feature Representations • Fundamentally, Attribute-Value pairs • Features: atomic symbols from a finite set • Values may be • Atomic symbols from a finite set Attribute-value matrix (AVM) NUMBER PL PERSON 3 NUMBER PL PERSON 3 CAT NP NUMBER PL PERSON 3

  33. Feature Representations • Fundamentally, Attribute-Value pairs • Features: atomic symbols from a finite set • Values may be • Atomic symbols from a finite set • Values may also be feature structures themselves Attribute-value matrix (AVM) CAT NP AGREEMENT NUMBER PL PERSON 3

  34. Feature Representations • Feature path: • Sequence of features through a feature structure leading to a particular value CAT NP AGREEMENT NUMBER PL PERSON 3

  35. Feature Representations • Feature path: • Sequence of features through a feature structure leading to a particular value CAT NP AGREEMENT NUMBER PL PERSON 3 <AGREEMENT NUMBER> -> PL

  36. Feature Representations • Feature path: • Sequence of features through a feature structure leading to a particular value CAT NP AGREEMENT NUMBER PL PERSON 3 <AGREEMENT NUMBER> -> PL <AGREEMENT PERSON> -> 3

  37. Feature Representations • Reentrant feature structures • Features share some feature structure as value • Not merely equal values • Shared substructure • Feature paths lead to same node CAT S HEAD AGREEM’T NUMBER PL PERSON 3 1 1 SUBJECT AGREEMENT

  38. AVM CAT NP AGREEMENT NUMBER PL NUMBER PL PERSON 3 PERSON 3 NUMBER PL PERSON 3 CAT S HEAD AGREEM’T NUMBER PL PERSON 3 1 CAT NP NUMBER PL PERSON 3 1 SUBJECT AGREEMENT

  39. Head-Subject Agreement CAT S HEAD AGREEM’T NUMBER PL PERSON 3 1 1 SUBJECT AGREEMENT

  40. Feature representations • Feature structures can also be represented as DAGs • Directed, acyclic graphs • Edges are features • Nodes values

  41. Reentrant DAG

  42. Unification • Two key roles:

  43. Unification • Two key roles: • Merge compatible feature structures

  44. Unification • Two key roles: • Merge compatible feature structures • Reject incompatible feature structures

  45. Unification • Two key roles: • Merge compatible feature structures • Reject incompatible feature structures • Two structures can unify if

  46. Unification • Two key roles: • Merge compatible feature structures • Reject incompatible feature structures • Two structures can unify if • Feature structures are identical • Result in same structure

  47. Unification • Two key roles: • Merge compatible feature structures • Reject incompatible feature structures • Two structures can unify if • Feature structures are identical • Result in same structure • Feature structures match where both have values, differ in missing or underspecified • Resulting structure incorporates constraints of both

  48. Subsumption • Relation between feature structures • Less specific f.s. subsumes more specific f.s. • F.s. F subsumes f.s. G iff • For every feature x in F, F(x) subsumes G(x) • For all paths p and q in F s.t. F(p)=F(q), G(p)=G(q)

  49. Subsumption • Relation between feature structures • Less specific f.s. subsumes more specific f.s. • F.s. F subsumes f.s. G iff • For every feature x in F, F(x) subsumes G(x) • For all paths p and q in F s.t. F(p)=F(q), G(p)=G(q) • Examples: • A: [Number SG], B: [Person 3] • C:[Number SG] • [Person 3]

  50. Subsumption • Relation between feature structures • Less specific f.s. subsumes more specific f.s. • F.s. F subsumes f.s. G iff • For every feature x in F, F(x) subsumes G(x) • For all paths p and q in F s.t. F(p)=F(q), G(p)=G(q) • Examples: • A: [Number SG], B: [Person 3] • C:[Number SG] • [Person 3] • A subsumes C

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