74.793 NLP and Speech 2004 Feature Structures

1. 74.793 NLP and Speech 2004Feature Structures Feature Structures and Unification

2. Feature Structures - General • Feature structures describe linguistic attributes or features like number, personassociated with words or syntactic constituents like noun phrase. • Feature structures are sets of features and values, e.g. hat [Number sing ] buys [Person 3 ] [Number sing ]

3. Feature Structures - Agreement Feature structures can be collected in one ‘variable’ called agreement. buys agreement [Person 3] [Number sing]

4. Feature Structures, Grammar, Parsing Feature Structures • describe additional syntactic-semantic information, like category, person, number, e.g. goes <verb, 3rd, singular> • specifyfeature structure constraints (agreements) as part of the grammar rules • during parsing, check agreements of feature structures (unification) example S → NP VP <NP number> = <VP number> S → NP VP <NP agreement> = <VP agreement>

5. Feature Structures as Constraints Ungrammatical sentences like “He go” or “We goes” can be excluded using feature constraints. example S → NP VP <NP agreement> = <VP agreement> S → NP VP <NP number> = <VP number> <NP person> = <VP person>

6. Feature Structures and Categories Add to feature structure category cat: buyscatverb agreement [Person 3 ] [Number sing]

7. Feature Structures and Unification 1 Compare and combine feature structures: he buys buyscatverb agreement [Person 3 ] [Number sing] hecatnoun agreement [Person 3 ] [Number sing]

8. Using Feature Structures S → NP VP <NP number> = <VP number> <NP person> = <VP person> buyscatverb agreement [Person 3 ] [Number sing] hecatnoun agreement [Person 3 ] [Number sing]

9. Unification of Feature Structures Agreement is checked by the unification operation according to the following rules: [featurei valuei] |_| [featurei valuei] = [featurei valuei] [featurei valuei] |_| [featurei valuej] = fail if valueivaluej [featurei valuei] |_| [featureiundef.] = [featurei valuei] [featurei valuei] |_| [featurej valuej] = featurei valuei featurej valuej if featurei featurej

10. Features and Subcategorization 1 NP modifiers or Verb complements central noun + modifiers +agreement central verb+complements+agreements “... the man who chased the cat out of the house ...” “... the man chased the barking dog who bit him ...” Agreements are passed on / inherited within phrases, e.g. agreement of VP derived from Head-Verb of VP: <VP agreement> determined by <Verb agreement> <NP agreement> determined by <Nom agreement>

11. Features and Subcategorization 2 NP modifiers or Verb complements: central noun+modifiers+agreement central verb+complements+agreements “... the manwho chased the cat out of the house ...” “... the man chased the barking dogwho bit him ...” Agreements are passed on / inherited within phrases, e.g. agreement of VP derived from Head-Verb of VP: <VP agreement> determined by <Verb agreement> <NP agreement> determined by <Nom agreement>

12. Semantics

13. Semantics Distinguish between • surface structure (syntactic structure) and • deep structure (semantic structure) of sentences. Different forms of Semantic Representation • logic formalisms • ontology / semantic representation languages • Case Frame Structures (Filmore) • Conceptual Dependy Theory (Schank) • DL and similar KR languages • Ontologies

14. Semantic Representations Semantic Representations based on some form of (formal) Representation Language. • Semantics Networks • Conceptual Dependency Graphs • Case Frames • Ontologies • DL and similar KR languages

15. Constructing a Semantic Representation General: • Start with surface structure • Derived from parser. • Map surface structure to semantic structure • Use phrases as sub-structures. • Find concepts and representations for central phrases (e.g. VP, NP, then PP) • Assign phrases to appropriate roles around central concepts (e.g. bind PP into VP representation).

16. Ontology (Interlingua) approach • Ontology: a language-independent classification of objects, events, relations • A Semantic Lexicon, which connects lexical items to nodes (concepts) in the ontology • An analyzer that constructs Interlinguarepresentations and selects (an?) appropriate one (based on Steve Helmreich's 419 Class, Nov 2003)

17. Semantic Lexicon • Provides a syntactic context for the appearance of the lexical item • Provides a mapping for the lexical itemto a node in the ontology (or more complex associations) • Provides connections from the syntactic context to semantic roles and constraints on these roles

18. Deriving Basic Semantic Dependency Deriving Basic Semantic Dependency (a toy example) Input: John makes tools Syntactic Analysis: cat verb tense present subject   root john cat noun-proper object   root     tool cat noun number plural

19. Lexicon Entries for Johnand tool John-n1 syn-struc root john cat noun-proper sem-struchuman name john gender male tool-n1 syn-struc root tool cat n sem-struc tool

20. Meaning Representation - Example make Relevant Extract from the Specification of the Ontological Concept Used to Describe the Appropriate Meaning of make: manufacturing-activity... agent human theme artifact …

21. Relevant parts of the (appropriate senses of the) lexicon entries for John and tool John-n1 syn-struc root john cat noun-proper sem-struchuman name john gender male tool-n1 syn-struc root tool cat n sem-struc tool

22. Semantic Dependency Component The basic semantic dependency component of the TMR forJohn makes tools manufacturing-activity-7 agent uman-3 theme set-1 element tool cardinality > 1 …

23. try-v3 syn-struc root try cat v subj root $var1 cat n xcomp root $var2 cat v form OR infinitive gerund sem-struc set-1 element-type refsem-1 cardinality >=1 refsem-1 sem event agent ^$var1 effect refsem-2 modality modality-type epiteuctic modality-scope refsem-2 modality-value < 1 refsem-2 value ^$var2 sem event

24. Constructing an IL representation For each syntactic analysis: • Access all semantic mappings and contextsfor each lexical item. • Create all possible semantic representations. • Test them for coherency of structure and content.

25. “Why is Iraq developing weapons of mass destruction?”

26. Word sense disambiguation • Constraint checking – making sure the constraints imposed on context are met • Graph traversal – is-a links are inexpensive • Other links are more expensive • The “cheapest” structure is the most coherent • Hunter-gatherer processing

27. Logic Formalisms Lambda Calculus

28. Semantics - Lambda Calculus 1 Logic representations often involve Lambda-Calculus: • represent central phrases (e.g. verb) as -expressions • -expression is like a function which can be applied to terms • insert semantic representation of complement or modifier phrases etc. in place of variables x, y: loves (x, y) FOPL sentence xy loves (x, y)-expression, function xy loves (x, y) (John)  y loves (John, y)

29. Semantics - Lambda Calculus 2 Transform sentence into lambda-expression: “AI Caramba is close to ICSI.” specific: close-to (AI Caramba, ICSI) general: x,y: close-to (x, y)  x=AI Caramba  y=ICSI Lambda Conversion: -expr: xy: close-to (x, y) (AI Caramba) Lambda Reduction: y: close-to (AI Caramba, y) close-to (AI Caramba, ICSI)

30. Semantics - Lambda Calculus 3 Lambda Expressions can be constructed from central expression, inserting semantic representations for complement phrases Verb  serves {xy e IS-A(e, Serving)  Server(e,y)  Served(e,x)} represents general semantics for the verb 'serve Fill in appropriate expressions for x, y, for example 'meat' for y derived from Noun in NP as complement to Verb.

31. References Jurafsky, D. & J. H. Martin, Speech and Language Processing, Prentice-Hall, 2000. (Chapters 9 and 10) Helmreich, S., From Syntax to Semantics, Presentation in the 74.419 Course, November 2003.