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Semantics in Sign-Based Construction Grammar
◮ Ling 226: Construction Grammar
◮ Instructor: Ivan A. Sag ([email protected])
◮ URL: http://lingo.stanford.edu/sag/L226
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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’
◮ ...
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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.
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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.
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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]
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More Lexical Ambiguity
◮ Homophony:
They can build a better pen.
She kicked the bucket.
That man is mad.
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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]
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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.
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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]
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Disambiguation is an unsolved research problem.
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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’
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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’
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semantics
◮ The values of sem are semantic objects
◮ But what do semantic objects (representations) look like?
◮ [reads(the(book))](the(boy))
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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
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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))
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◮ 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
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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))))
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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
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Computational Implications
◮ Scope ambiguity explodes.
◮ Disambiguation is an unsolved research problem.
◮ Translation often preserves ambiguity.
◮ Hence, scope-free representations facilitate machinetranslation.
◮ This led to Minimal Recursion Semantics (MRS).
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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
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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.
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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.
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We’ll work up to the underspecification analysis of
quantifiers gradually,
first introducing semantic features of the sign,
situations, and frame semantics.
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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.
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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
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A Semantic Object
sem-obj
index s
ltop l1
frames
⟨
eating-fr
label l1
sit s
ingestor i
ingestible j
⟩
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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.
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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
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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, ...
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Commercial Transaction Frame
◮ Frame Elements: buyer, seller, money, goods
◮ buy, sell, pay, spend, cost, purchase, give, get, ...
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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.
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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]
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Davis-Koenig Style Analysis
frame
...
comm-trans-fr
...
actor-fr
actor-undgr-fr
...
buy-fr sell-fr ...
...
undgr-fr
...
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A Listeme
cn-lxm
form 〈 book 〉
sem
sem-obj
index i
frames
⟨
book-fr
label l
entity i
⟩
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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
⟩
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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
〉
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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
〉
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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
〉
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◮ l0 = l1, l3 = l6, and l5 = l7
◮ l0 = l3, l1 = l7, and l5 = l6
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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.
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context
◮ Context-objects may be specified as follows:
context
c-inds
spkr index
addr index
utt-loc index
. . .
bckgrnd list(proposition)
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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 〉
]
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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
〉
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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
〉
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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.
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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 ...
⟩
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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 ...
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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.
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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)
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Why are Certain Constructs Licensed and Not Others?
◮ The particular inventory of Combinatory Constructions
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Some Types of Lexical Combinatoric Constructs
linguistic-object
... construct
lexical-cxt
deriv-cxt
...
compound-noun-cxt passive-cxt
postinfl-cxt
...
infl-cxt
...
preterite-cxt
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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 〉
. . .
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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
⟩
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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 〉]
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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
...
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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
]
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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
...
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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
...
...
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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
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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 〉
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How to Show Sign Well-Formedness?
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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 . . .
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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 . . .
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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 . . .
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◮ 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
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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
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Head-Functor Construction:
hd-func-cxt ⇒
mtr [syn X ! [mrkg M ]]
dtrs
⟨
syn
[
cat [select Y ]
mrkg M
]
, Y :[syn X ]
⟩
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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
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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
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