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Semantics in Sign-Based Construction Grammar
◮ Ling 7800-065: Sign-Based Construction Grammar
◮ Instructor: Ivan A. Sag ([email protected])
◮ URL: http://lingo.stanford.edu/sag/LI11-SBCG
Semantics
◮ ‘Formal’ Semantics: Entailment, truth conditions
e.g. Montague (possible-world) semantics, Davidsonian(‘event’-based) Semantics, Situation Semantics1, SituationSemantics2, Dynamic Semantics, ...
◮ Lexical Semantics: Lexical relations, lexical entailments,semantic roles, diathesis alternations, ....
Lexical decomposition analysis,
◮ Characterization of ambiguity
◮ Semantic ‘Linking’
◮ ...
Structural Ambiguity 1
◮ Attachment Ambiguity:
I forgot how good beer tastes.
I saw the man with the telescope.
◮ Verb Class Ambiguity:
Teddy is the man that I want to succeed.
They gave away the letter to Jones.
Structural Ambiguity 2
◮ Complement vs. Adjunct Ambiguity:
I found the boy hopping on one foot.
I can’t see wearing my eyeglasses.
◮ Coordination Ambiguity:
This offer applies to old men and women.
Every husband and father should pay attentionto this.
Lexical Ambiguity
◮ Category Ambiguity:
a draft/to draft; a can/to can
◮ Polysemy:
I want a light beer.
It must be tough to lose a wife.(-Yes, practically impossible.) [Groucho Marx, 1961]
More Lexical Ambiguity
◮ Homophony:
They can build a better pen.
She kicked the bucket.
That man is mad.
Interactions
In addition, there are many interactions ofthese ambiguities which involve bothstructural and lexical ambiguity:
◮ I saw her duck.
◮ The only thing capable of consuming thisfood has four legs and flies. [M.A.S.H. rerun]
◮ I saw that gas can explode.
◮ I can have any guy I please (unfortunately Idon’t please any of them). [Lily Tomlin]
Ambiguity of Scope:
◮ Jones has found a defect in every Toyotawith over 100,000 miles.
◮ Dukakis agrees to only two debates. (SFChronicle, 1988)
◮ Everyone in the room speaks at least twolanguages.
Ambiguity of Ellipsis:
◮ Jan likes Dana more than Lou .
◮ Nothing makes you feel as good as gold.[Jewelry commercial-1988]
◮ Jones thought the yacht was longer than it is.[Bertrand Russell]
◮ McCain claims he’ll solve all the world’s problems more oftenthan Edwards does .[Strict vs. ‘Sloppy’ Identity]
Disambiguation is an unsolved research problem.
phonology /kIm/
arg-st 〈 〉
syntax NP
semantics ‘the intended person named Kim’
phonology /læf-d/
arg-st 〈 NP 〉
syntax V[fin]
semantics ‘a laughing event situated
prior to the time of utterance’
phonology /Evri lIngwIst/
syntax NP
semantics ‘the set of properties all linguists share’
phonology /pæt læf-d/
syntax S[fin]
‘the proposition that there was a laughing event
semantics situated prior to the time of utterance where a
certain person named Pat did the laughing’
semantics
◮ The values of sem are semantic objects
◮ But what do semantic objects (representations) look like?
◮ [reads(the(book))](the(boy))
complex-funct-expr
functor
complex-funct-expr
functor readsafe
arg
complex-funct-expr
functor theafe
arg bookare
arg
complex-funct-expr
functor theafe
arg boyare
Functional Semantic Structure
sem-expr
functor-expr
atomic-funct-expr complex-funct-expr
complex-expr ref-expr
complex-ref-expr atomic-ref-expr
complex-expr :
[
FUNCTOR functor-expr
ARG sem-expr
]
laughsafe(kimare), laughsafe(theafe(boyare))[readsafe(theafe(bookare))](theafe(boyare))
◮ SBCG is very flexible
◮ Compatible with almost any kind of semantics, if it isformulated with sufficient precsion.
◮ Montague Semantics
◮ (Barwise-Perry style) Situation Semantics
◮ Frame Semantics
◮ Any adequate semantic framework has to deal withquantifiers, in particular generalized quantifiers
Two Notations for Generalized Quantifiers
◮ (some i, student(i))(every j, answer(j))(know(i,j))
◮ (every j, answer(j))(some i, student(i))(know(i,j))
or:
◮ (some, i, student(i), (every, j, answer(j), (know(i,j))))
◮ (every, j, answer(j), (some, i, student(i), (know(i,j))))
Two Scopings of a Doubly Quantified S
S1
some i S2
student i
S3
every j S4
answer j
S5
know i j
S1
every j S4
answer j
S3
some i S2
student i
S5
know i j
Computational Implications
◮ Scope ambiguity explodes.
◮ Disambiguation is an unsolved research problem.
◮ Translation often preserves ambiguity.
◮ Hence, scope-free representations facilitate machinetranslation.
Psycholinguistic Motivation
◮ Multiple interpretations - Two solutions:
1. process two alternative representations in parallel
2. a single underspecified representation that is can be resolved,once further information is available.
◮ Some ambiguities work one way; some the other
Psycholinguistic Motivation
◮ Frazier and Raynor, 1990.
Apparently, the book didn’t sell, after having so many pagestorn.
Apparently, the book didn’t sell, after taking so long to write.
◮ The physical object sense of book and the textual objectsense are two resolutions of the lexically specified meaning ofthe noun book.
◮ Different results for two meanings of bank.
◮ Psycholinguistic criteria for ambiguity vs. underspecification.
Psycholinguistic Motivation
◮ Tunstall 1998 shows that underspecification is more plausiblefor quantifier scope ambiguities:
◮ Kelly showed every photo to a critic last month.◮ The critic was from a major gallery.
◮ Kelly showed every photo to a critic last month.◮ The critics were from a major gallery.
We’ll work up to the underspecification analysis of
quantifiers gradually,
first introducing semantic features of the sign,
situations, and frame semantics.
semantics
The values of sem are semantic objects, which are specified for thefollowing 3 features:
◮ index is used to identify the referent of an expression. Itsvalue is an index, functioning essentially as a variable assignedto an individual in the case of an NP or a situation in the caseof VPs or Ss.
◮ ltop (local-top) takes a label of a frame as its argument.This label is the ‘top’ frame in the resolved semantics of asentence viewed as a rooted tree.
◮ The feature frames is used to specify the list of predicationsthat together determine the meaning of a sign. The value offrames is a (possibly empty) list of frames.
Situations
◮ Donald Davidson: event-based semantics
◮ Love(Kim,Sandy) ; Some e [Love(e,Kim,Sandy)]
◮ Some e [Love(e) & Lover(e,Kim) & Loved(e,Sandy)]
◮ Some e [Love(e) & Agent(e,Kim) & Goal(e,Sandy)]
◮ eventality: event or state
◮ situation ≈ eventuality
A Semantic Object
sem-obj
index s
ltop l1
frames
⟨
eating-fr
label l1
sit s
ingestor i
ingestible j
⟩
Frame Semantics
◮ Fillmore, Charles J. 1982. Frame Semantics. In Linguistics inthe Morning Calm, pages 111-137, Seoul: Hanshin PublishingCo.
◮ Fillmore, Charles J. 1985. Frames and the Semantics ofUnderstanding. Quaderni di Semantica 6, 222-254.
◮ Framenet: http://framenet.icsi.berkeley.edu/
◮ Fillmore, Charles J. and Baker, Colin. 2010. A FramesApproach to Semantic Analysis. In B. Heine and H. Narrog(eds.), The Oxford Handbook of Linguistic Analysis, pages313-340, Oxford: Oxford University Press.
◮ Fillmore, Charles J., Johnson, Christopher R. and Petruck,Miriam R.L. 2003. Background to Framenet. InternationalJournal of Lexicography 16.3, 235-250.
What Frame Semanticists say about It
◮ Rejection of ‘checklist’ (truth-conditional) theories of meaning
◮ Must understand semantic particulars in terms of broaderconceptual system
◮ Must understand members of a ‘contrast set’ in terms of allmembers of the set (Semantic Field Theory - Trier)
◮ Incorporates AI-notion of frame (stereotypic particular)
◮ Committed to experience-driven schematization
◮ Based on ‘Case Roles’ derived from Fillmore’s work on CaseGrammar
Self-Motion Frame
◮ Frame Elements: self-mover, source, path, goal,
manner, distance, area
◮ bop, bustle, crawl, dart, dash, hike, hobble, hop, jaunt, jog,lope, lumber, march, mince, saunter, scamper, scramble,shuffle, skip, slalom, slither, slog, sneak, sprint, stagger, step,stomp, stride, stroll, strut, stumble, swagger, swim, tiptoe,toddle, traipse, tramp, troop, trudge, trundle, waddle, wade,walk, wander, ...
Commercial Transaction Frame
◮ Frame Elements: buyer, seller, money, goods
◮ buy, sell, pay, spend, cost, purchase, give, get, ...
Commercial-Transaction Frame
comm-transaction-fr :
buyer ind
seller ind
money thing
goods thing
Different verbs involve different ‘profiling’:
◮ buy: actor = buyer = xarg.
◮ sell: actor = seller = xarg.
Davis-Koenig Style Analysis
frame
...
actor-fr
actor-undgr-fr
...
buy-fr sell-fr hit-fr love-fr ...
...
undgr-fr
...
actor-fr : [actor ind] undgr-fr : [undergoer ind]
Davis-Koenig Style Analysis
frame
...
comm-trans-fr
...
actor-fr
actor-undgr-fr
...
buy-fr sell-fr ...
...
undgr-fr
...
A Listeme
cn-lxm
form 〈 book 〉
sem
sem-obj
index i
frames
⟨
book-fr
label l
entity i
⟩
form 〈 every, book 〉
sem
sem-obj
index i
ltop l0
frames
⟨
every-fr
label l1
bv i
restr l2
scope l3
,
book-fr
label l2
entity i
⟩
sem-obj
ltop l1
frames 〈
some-fr
label l1
bv i
restr l2
scope l3
,
student-fr
label l2
entity i
,
every-fr
label l3
bv j
restr l4
scope l5
,
answer-fr
label l4
entity j
,
knowing-fr
label l5
cognizer i
cognized j
〉
sem-obj
ltop l3
frames 〈
some-fr
label l1
bv i
restr l2
scope l5
,
student-fr
label l2
entity i
,
every-fr
label l3
bv j
restr l4
scope l1
,
answer-fr
label l4
entity j
,
knowing-fr
label l5
cognizer i
cognized j
〉
sem-obj
ltop l0�5
frames 〈
some-fr
label l1
bv i
restr l2
scope l6
,
student-fr
label l2
entity i
,
every-fr
label l3
bv j
restr l4
scope l7
,
answer-fr
label l4
entity j
,
knowing-fr
label l5
cognizer i
cognized j
〉
◮ l0 = l1, l3 = l6, and l5 = l7
◮ l0 = l3, l1 = l7, and l5 = l6
Conclusions
◮ 1st Step
◮ Achieves Desired Design Properties
◮ Can Deal With Simple Structures
◮ Compatible with almost any representation scheme
◮ Copestake, Ann, Dan Flickinger, Carl Pollard, and Ivan A.Sag. 2006. Minimal Recursion Semantics: an Introduction.Research on Language and Computation 3.4: 281–332.
context
◮ Context-objects may be specified as follows:
context
c-inds
spkr index
addr index
utt-loc index
. . .
bckgrnd list(proposition)
Licensing Words
◮
pro-wd
form 〈 I 〉
sem [ind i ]
context [c-inds [spkr [ind i ]]]
◮
pro-wd
form 〈 we 〉
sem [ind g ]
context
[
c-inds [spkr [ind i ]]
bckgrnd {i ∈ g}
]
◮
[
pn-wd
form 〈 Kim 〉
]
A Lexical Class Construction
pn-wd ⇒
form L
syn
cat
noun
select none
xarg none
val 〈 〉
mrkg def
sem
[
ind i
frames 〈 〉
]
cntxt
bckgrnd
〈
some-fr
bv j
restr l2
scope l6
,
naming-fr
label l2
entity j
name L
,
equal-fr
label l6
arg1 j
arg2 i
〉
A Listemically Licensed Sign
pn-wd
form 〈Kim〉
syn
cat
noun
select none
xarg none
val 〈 〉
mrkg def
sem
[
ind i
frames 〈 〉
]
cntxt
bckgrnd
〈
some-fr
bv j
restr l2
scope l6
,
naming-fr
label l2
entity j
name 〈Kim〉
,
equal-fr
label l6
arg1 j
arg2 i
〉
Constructs
◮
construct :
[
mtr sign
dtrs nelist(sign)
]
◮ The mother(mtr) feature is used to place constraints onthe set of signs that are licensed by a given construct.
◮ The feature daughters(dtrs) specifies information aboutthe one or more signs that contribute to the analysis of aconstruct’s mother; the value of dtrsis a nonempty list ofsigns.
A Phrasal Construct (AVM Notation)
subj-pred-cl
mtr
phrase
form 〈 Obama, actually, won 〉
syn S
sem ...
dtrs
⟨
form 〈 Obama 〉
syn NP
sem ...
,
phrase
form 〈 actually, won 〉
syn VP
sem ...
⟩
A Phrasal Construct (Tree Notation)
subj-pred-cl
phrase
form 〈 Obama, actually, won 〉
syn S
sem ...
form 〈 Obama 〉
syn NP
sem ...
phrase
form 〈 actually, won 〉
syn VP
sem ...
The Sign Principle:
Every sign must be listemically or constructionally licensed, where:
a. a sign is listemically licensed only if it satisfies some listeme,and
b. a sign is constructionally licensed only if it is the mother ofsome well-formed construct.
The Grammar
◮ A set of listemes (sign descriptions)
◮ A set of constructions of the form:
τ ⇒ D (Every FS of type τ must satisfy D),
where either:
a. τ is a subtype of lexical-sign(Lexical Class Construction), or
b. τ is a subtype of construct(Combinatory Construction)
Why are Certain Constructs Licensed and Not Others?
◮ The particular inventory of Combinatory Constructions
Some Types of Lexical Combinatoric Constructs
linguistic-object
... construct
lexical-cxt
deriv-cxt
...
compound-noun-cxt passive-cxt
postinfl-cxt
...
infl-cxt
...
preterite-cxt
A Lexically Licensed Lexeme
strans-v-lxm
form 〈 love 〉
syn
cat
verb
vf fin
aux −
xarg 1
select none
mrkg unmk
val 〈 1 , NPj 〉
arg-st 〈 1 NPi , NPj 〉
. . .
Preterite Construction (↑infl-cxt)
preterite-cxt ⇒
mtr
form 〈 Fpret(X ) 〉
syn Y : [cat [vf fin]]
sem
ind s
ltop l2�0
frames
⟨
some-fr
lbl l0
bv s
restr l1
,
past-fr
lbl l1
arg s
⟩
⊕ L
dtrs
⟨
form 〈 X 〉
arg-st 〈NP[nom] , . . . 〉
syn Y
sem
ind s
ltop l2
frames L
⟩
A Preterite Construct
preterite-cxt
word
form 〈loved〉
syn 1
sem|frames
⟨
some-fr
lbl l0
bv s
restr l1
scope l2
,
past-fr
lbl l1
arg s
, 2
loving-fr
lbl l2
sit s
actor i
undgr j
⟩
strans-v-lxm
form 〈 love 〉
syn 1
sem [frames 〈 2 〉]
Some Types of Phrasal Combinatoric Constructs
linguistic-object
... construct
phrasal-cxt
headed-cxt
head-comp-cxt
pred-hd-comp-cxt sat-hd-comp-cxt
subj-head-cxt
... subj-pred-cl
...
Predicational Head-Complement Construction (↑hd-cxt):
pred-hd-comp-cxt ⇒
mtr [syn X ! [val 〈Y 〉]]
dtrs 〈Z 〉 ⊕ L :nelist
hd-dtr Z :
word
syn X :
[
cat [xarg Y ]
val 〈Y 〉 ⊕ L
]
pred-hd-comp-cxt
phrase
form 〈 loves, them 〉
syn
cat 3
verb
vf fin
aux −
xarg 1 NP
select none
val 〈 1 〉
word
form 〈 loves 〉
syn
cat 3
verb
vf fin
aux −
xarg NP
select none
val 〈 1 , 2 〉
2
word
form 〈 them 〉
syn
cat
noun
case acc
...
Some Types of Phrasal Construct (Ginzburg/Sag 2000)
linguistic-object
... construct
phrasal-cxt
headed-cxt
head-comp-cxt
pred-hd-comp-cxt
subj-head-cxt
...
subj-pred-cl
...
clause
core-cl
declarative-cl
...
...
Subject-Predicate Construction (↑subj-head-cxt)
subj-pred-cl ⇒
mtr [syn Y ! [val 〈 〉 ] ]
dtrs
⟨
X , Z :
syn Y :
cat
[
vf fin
aux −
]
mrkg unmk
val 〈 X 〉
⟩
hd-dtr Z
subj-pred-cl
phrase
form 〈 Obama, loved, them 〉
syn
cat 2
verb
vf fin
aux −
xarg NP
select none
1
word
form 〈 Obama 〉
syn
cat
noun
case nom
...
phrase
form 〈 loved, them 〉
syn
cat 2
verb
vf fin
aux −
xarg NP
select none
val 〈 1 〉
How to Show Sign Well-Formedness?
phrase
form〈 Obama , loved, them 〉
syn
[
cat 2
val 〈 〉
]
sem . . .
1
word
form〈 Obama 〉
syn NP
sem . . .
phrase
form〈 loved, them〉
syn
[
cat 2
val 〈 1 〉
]
sem . . .
word
form〈 loved 〉
syn
[
cat 2
val 〈 1 , 2 〉
]
sem . . .
2
word
form〈 them 〉
syn NP
sem . . .
phrase
form〈 Obama , loved, them 〉
syn
[
cat 2
val 〈 〉
]
sem . . .
1
word
form〈 Obama 〉
syn NP
sem . . .
phrase
form〈 loved, them〉
syn
[
cat 2
val 〈 1 〉
]
sem . . .
word
form〈 loved 〉
syn
[
cat 2
val 〈 1 , 2 〉
]
sem . . .
2
word
form〈 them 〉
syn NP
sem . . .
An Analysis Tree
phrase
form〈 Obama , loved, them 〉
syn
[
cat 2
val 〈 〉
]
sem . . .
1
word
form〈 Obama 〉
syn NP
sem . . .
phrase
form〈 loved, them〉
syn
[
cat 2
val 〈 1 〉
]
sem . . .
word
form〈 loved 〉
syn
[
cat 2
val 〈 1 , 2 〉
]
sem . . .
2
word
form〈 them 〉
syn NP
sem . . .
◮ Analysis Tree merely a demonstration of the grammar’soutput.
◮ A proof.
◮ Trees are not linguistic objects; they’re metaobjects.
◮ Therefore, you might expect them not to be the locus ofgrammatical constraints.
◮ Binding Theory
Some marking Values (bis)
marking the most general marking value - a supertype of the restunmk phrases that aren’t marked, e.g. we readthan compared phrases, e.g. than we readas equated phrases, e.g. as I couldof some of-phrases, e.g. of minedet ‘determined’ nominal signs (see below)a a subtype of det, e.g. a bookdef definite nominal signs, i.e. the table, Prince, we
Head-Functor Construction:
hd-func-cxt ⇒
mtr [syn X ! [mrkg M ]]
dtrs
⟨
syn
[
cat [select Y ]
mrkg M
]
, Y :[syn X ]
⟩
hd-func-cxt
form 〈a, puppy〉
syn
cat 3
[
noun
select none
]
val L 〈 〉
mrkg 2 a
form 〈a〉
syn
cat
[
det
select 1
]
mrkg 2 a
1
form 〈puppy〉
syn
cat 3
[
noun
select none
]
val L 〈 〉
mrkg unmk
hd-func-cxt
form 〈happy, puppy〉
syn
cat 3
[
noun
select none
]
val L 〈 〉
mrkg 2 unmk
form 〈happy〉
syn
cat
[
adj
select 1
]
mrkg 2 unmk
1
form 〈puppy〉
syn
cat 3
[
noun
select none
]
val L 〈 〉
mrkg unmk
