1. 74.419 Artificial Intelligence 2004
From Syntax to Semantics
Grammatical Extensions
Sentence Structures
Noun Phrase - Modifications
Verb Phrase - Subcategorization
Feature Structures
-expressions

2. Sample Grammar Task: Parse "Does this flight include a meal?"
Grammar (S, NT, T, P) –
Part-of-Speech  NT, syntactic Constituents  NT
S → NP VP statement
S → Aux NP VP question
S → VP command
NP → Det Nominal
NP → Proper-Noun
Nominal → Noun | Noun Nominal | Nominal PP
VP → Verb | Verb NP | Verb PP | Verb NP PP
PP → Prep NP
Det → that | this | a
Noun → book | flight | meal | money
Proper-Noun → Houston | American Airlines | TWA
Verb → book | include | prefer
Aux → does
Prep → from | to | on
Task: Parse "Does this flight include a meal?"

3. Sample Parse Tree Task: Parse "Does this flight include a meal?"
Aux NP VP
Det Nominal Verb NP
Noun Det Nominal
does this flight include a meal

4. Grammar – 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?”

5. Grammar – Noun Phrase Modification 1
Noun Phrase Modifiers
head = the central noun of the NP (+ modifiers)
modifiers before the head noun (prenominal)
determiner the, a, this, some, ...
predeterminer all the flights
cardinal numbers, ordinal numbers one flight, the first flight, ...
quantifiers much, little
adjectives a first-class flight, a long flight
adjective phrase the least expensive flight
NP → (Det) (Card) (Ord) (Quant) (AP) Nominal

6. Grammar – Noun Phrase Modification 2
Noun Phrase Modifiers (continued)
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
Verb Phrase and Subategorization
VP = Verb + other constituents. Different verbs accept or need different constituents → Verb Subcategorization; captured in verb frames.
sentential complement VP  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
describe additional syntactic-semantic information, like category, person, number, e.g. goes  (verb, 3rd, singular)
specify feature structure constraints (agreements) as part of grammar
during parsing, check agreements of feature structures (unification)
example
S → NP VP <NP number> = <VP number>
or S → NP VP <NP agreement> = <VP agreement>

9. Grammar – Feature Structures 2
agreements in general determined by head of phrase, i.e. central noun or verb
example
(1) “... the man who chased the cat out of the house ...”
central noun?
(2) “... the man chased the barking dog who bit him ...”
central verb?
operations on agreements (e.g. sing |_| plural = fail)
unification for checking of specified agreements

10. Semantics
Distinguish between surface structure (syntactic structure) and deep structure (semantic structure) of sentences.
Semantics can be defined based on FOPL.
Transform sentence (with quantified variables) into lambda-expression. Central is again verb (with roles).
lambda-expression is like a function which can then be applied to constants.
example: x, y: loves (x, y) FOPL sentence
-expr: xy loves (x, y) function
xy loves (x, y) (John)  y loves (John, y)
Note: The semantics of LISP is based on lambda-calculus.

11. Semantics
Distinguish between surface structure (closer to syntactic structure) and deep structure (semantic structure) of sentences
Semantic can be defined based on FOPL.
Transform sentence (with quantified variables) into lambda-expression. Central is again verb (with roles).
lamda-expression is like a function which can then be applied to constants.
“AI Caramba is close to ICSI.”
specific: close-to (AI Caramba, ICSI)
general: x,y: close-to (x, y)  x=AI Caramba  y=ICSI
-expr: xy: close-to (x, y) (AI Caramba)
y: close-to (AI Caramba, y)
close-to (AI Caramba, ICSI)

12. Additional References
Jurafsky, D. & J. H. Martin, Speech and Language Processing, Prentice-Hall, (Chapters 9 and 10)
Earley Algorithm
Jurafsky & Martin, Figure 10.16, p.384
Earley Algorithm - Examples
Jurafsky & Martin, Figures and 10.18