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Features and Augmented Grammars

This document discusses the challenges of using traditional Context-Free Grammars (CFGs) to describe certain linguistic phenomena, such as number agreement between constituents. It introduces augmented CFGs that utilize feature structures to more naturally represent constraints like agreement in number and gender. Key aspects include defining constituents as feature structures, sharing features, and using complex feature structures to account for ambiguity. The paper outlines grammatical parsing techniques that enhance CFGs with features, showcasing examples and rule formulations for better parsing of English syntax.

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Features and Augmented Grammars

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  1. Features and Augmented Grammars Allen’s Chapter 4 J&M’s Chapter 11

  2. Augmenting CFGs with Features • Certain linguistic constraints are not naturally described via CFGs • Example: Number Agreement between constituents - “a boys” • Possible to describe using refined CF rules: NP-Sing --> ART-Sing N-Sing NP-Plural --> ART-Plural N-Plural • Much more natural to describe via a single feature Number in an augmented CF rule: NP --> ART N (only when Number1 = Number2)

  3. Feature Structures • Constituents can be defined as feature structures that map features to values • Features can be shared between constituents • Some basic features for English: – Number, Gender and Person agreement – Verb form features and sub-categorizations • Complex Feature Structures: Feature values can themselves be feature structures

  4. An Example ART1: (CAT ART ROOT a NUMBER s) Represents a particular use of word a Often summarized as: ART1: (ART ROOT a NUMBER s)

  5. Larger constituents: Representing an NP constituent for the phrase a fish NP1: (NP NUMBER s 1 (ART ROOT a NUMBER s) 2 (N ROOT fish NUMBER s))

  6. Feature Structure

  7. Augmented Rules (NP NUMBER ?n)  (ART NUMBER ?n) (N NUMBER ?n) *(NP 1 (ART NUMBER s) 2 (N NUMBER s)) *(NP NUMBER s 1 (ART NUMBER s) 2 (N NUMBER p))

  8. Using Variables to Express ambiguities (N ROOT fish NUMBER ?n) (N ROOT fish NUMBER ?b {s, p}) (N ROOT fish NUMBER {s, p})

  9. Person and Number Features

  10. PForm Feature

  11. Verb Sub categorizations:SUBCAT Feature

  12. SUBCAT Examples: (VP)  (V SUBCAT _np_vp:inf) (NP) (VP VFORM inf) (VP)  (V SUBCAT _np_pp:loc) (NP) (PP PFORM LOC)

  13. Binary Features Whether the constituent does or does not have a feature INV feature has two values + and – Jack Laughed has an INV feature with value – = (-INV) Did Jack Laugh has an INV feature with value + = (+INV)

  14. Morphological Analysis

  15. Sample Grammar (Abbreviated Form)

  16. Sample Grammar (Detailed Form)

  17. Parsing with Features • (NP AGR ?a)   (ART AGR ?a) (N AGR ?a) • (ART ROOT A AGR 3s) • (NP AGR 3s)   (ART AGR 3s) (N AGR 3s) • (NP AGR 3s)  (ART AGR 3s)  (N AGR 3s) • (N ROOT DOG1 AGR 3s) • (NP AGR 3s)  (ART AGR 3s) (N AGR 3s) 

  18. Parsing with Features • Given an arc A, and NEXT as the constituent following , and a new constituent X, which is used to extend A • Find an instantiation of variables such that all features specified in NEXT are found in X. • Create a new arc A’, which is a copy of A except for the instantiations of the variables determined in step 1 • Update A’ as usual in a Chart parser

  19. Parse Trees with Features

  20. Chart with Features

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