2. 74.419 Artificial Intelligence 2005/06 From Syntax to Semantics

3.  Grammatical Extensions  Sentence Structures  Noun Phrase - Modifications  Verb Phrase - Subcategorization  Feature Structures  -expressions

4. Grammar – Sentence Level Constructs Sentence Level Constructs declarativeS  NP VP “ This flight leaves at 9 am. ” imperativeS  VP “ Book this flight for me. ” yes-no-questionS  Aux NP VP “ Does this flight leave at 9 am? ” wh-questionS  Wh-NP Aux NP VP “ When does this flight leave Winnipeg? ”

5. 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 adjectivesa first-class flight, a long flight adjective phrasethe least expensive flight NP  (Det) (Card) (Ord) (Quant) (AP) Nominal

6. 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

7. 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 complement VP  Verb NP I want this flight. no complement VP  Verb I sleep. more forms VP  Verb PP PP I fly from Boston to Chicago.

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

9. 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 determined by

10. 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

11. 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)FOPLsentence x y loves (x, y) -expression, function x y loves (x, y) (John)  y loves (John, y)

12. 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)

13. 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. eventsubject-NPobject-NP

14. 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

15. Deriving basic semantic dependency (a toy example) Input: John makes tools Syntactic Analysis: catverb tensepresent subject root john catnoun-proper object root tool catnoun numberplural

16. John-n1 syn-struc rootjohn catnoun-proper sem-struc human name john gendermale tool-n1 syn-struc roottool catn sem-struc tool Relevant parts of the (appropriate senses of the) lexicon entries for John and tool

17. 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.

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