74.419 Artificial Intelligence 2005/06

1. 74.419 Artificial Intelligence 2005/06 From Syntax to Semantics

2. From Syntax to Semantics • Grammatical Extensions • Sentence Structures • Noun Phrase - Modifications • Verb Phrase - Subcategorization • Feature Structures • -expressions

3. Grammar – Sentence Level Constructs Sentence Level Constructs • declarative S  NP VP “This flight leaves at 9 am.” • imperative S  VP “Book this flight for me.” • yes-no-question S  Aux NP VP “Does this flight leave at 9 am?” • wh-question S  Wh-NP Aux NP VP “When does this flight leave Winnipeg?”

4. Grammar – Noun Phrase Modification 1 head = the central noun of the NP (+ modifiers) • modifiers before the head noun (prenominal) • determinerthe, a, this, some, ... • predeterminerall the flights • cardinal numbers, ordinal numbersone flight, the first flight, ... • quantifiersmuch, little • adjectives a first-class flight, a long flight • adjective phrase the least expensive flight NP  (Det) (Card) (Ord) (Quant) (AP) Nominal

5. Grammar – Noun Phrase Modification 2 • modifiers after the head noun (post-nominal) • prepositional phrase PP all flights from Chicago Nominal  Nominal PP (PP) (PP) • non-finite clause, gerundive postmodifers all flights arriving after 7 pm Nominal  GerundVP GerundVP  GerundV NP | GerundV PP | ... • relative clause a flight that serves breakfast Nominal  Nominal RelClause RelClause  (who | that) VP

6. Grammar – Verb Subcategorization VP = Verb + other constituents. Different verbs accept or need different constituents → Verb Subcategorization; captured in verb frames. • sentential complementVP  Verb inf-sentence I want to fly from Boston to Chicago. • NP complementVP  Verb NP I want this flight. • no complement VP  Verb I sleep. • more forms VP  Verb PP PP I fly from Boston to Chicago.

7. Grammar – Feature Structures 1 Feature Structures • describe additional syntactic-semantic information, like category, person, number, e.g. goes  <verb, 3rd, singular> • specify feature structure constraints (agreements) as part of the grammar rules • during parsing, check agreements of feature structures (unification) e.g. S  NP VP <NP number>=<VP number> or S  NP VP <NP agreement>=<VP agreement>

8. Grammar – Feature Structures 2 Sub-categories specify attached phrases, e.g. NP modifiers or Verb complements like NP “... the man who chased the cat out of the house ...” central noun + sub-categories + agreements “... the man chased the barking dog who bit him ...” central verb+ sub-categories + agreements Agreements are passed on / inherited within phrases, e.g. agreement of VP derived from Head-Verb of VP, through special Unification functions <VP agreement> determined by <Verb agreement> <NP agreement> determined by <Nom agreement>

9. Semantics Distinguish between • surface structure (syntactic structure) and • deep structure (semantic structure) of sentences. Different forms of Semantic Representation • logic based • ontology based / semantic language / interlingua • Case Frame structures • DL and similar KR languages • linguistics based Ontologies

10. Semantics - Lambda Calculus 1 Logic representations often involve Lambda-Calculus: • represent central phrases (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)

11. 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: x y: close-to (x, y) (AI Caramba) Lambda Reduction: y: close-to (AI Caramba, y) close-to (AI Caramba, ICSI)

12. Semantics - Lambda Calculus 3 Lambda Expressions can be constructed from central (VP) expression, inserting semantic representations for complement (NP, PP) 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. event subject-NP object-NP

13. InterLingua (IL) approach • An Ontology, a language-independent classification of objects, event, relations • A Semantic Lexicon, which connects lexical items to nodes (concepts) in the ontology • An analyzer that constructs IL representations and selects (an?) appropriate one

14. 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

15. Relevant parts of the (appropriate senses of the) 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

16. Semantics Semantic Representation through: • Case Frame structures • DL and similar KR languages • linguistics based Ontologies General: Map surface structure to semantic structure • Derive phrases as sub-structures • Find concepts for central phrases (VP, NP) • Assign phrases to appropriate roles around central concepts.

17. Additional References Jurafsky, D. & J. H. Martin, Speech and Language Processing, Prentice-Hall, 2000. (Chapters 9 and 10)