what does language do? “harry walked to the cafe.” “harry walked into the cafe.” a sentence...

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What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences: CAFE CAFE Goal of action = at cafe Source = away from cafe cafe = point- like location Goal of action = inside cafe Source = outside cafe cafe = containing location

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Page 1: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

What does language do?

“Harry walked to the cafe.” “Harry walked into the cafe.”

A sentence can evoke an imagined scene and resulting inferences:

CAFE CAFE

– Goal of action = at cafe– Source = away from cafe– cafe = point-like location

– Goal of action = inside cafe– Source = outside cafe– cafe = containing location

Page 2: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Language understanding

Interpretation

(Utterance, Situation)

Linguistic knowledge

Conceptual knowledge

Analysis

Page 3: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Language understanding: analysis & simulation

“Harry walked to the cafe.”

Schema Trajector Goalwalk Harry cafe

Cafe

Lexicon

Constructicon

General Knowledge

Belief State

Analysis Process

SemanticSpecification

Utterance

Simulation

Page 4: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Interpretation: x-schema simulation

Constructions can• specify which

schemas and entities are involved in an event, and how they are related

• profile particular stages of an event

• set parameters of an event

energy

walker at goal

walker=Harry goal=home

Harry is walking home.

Page 5: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Phonetics

Semantics

Pragmatics

Morphology

Syntax

Traditional Levels of Analysis

Page 6: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Phonetics

Semantics

Pragmatics

Morphology

Syntax

“Harry walked into the cafe.”

Utterance

Page 7: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Construction Grammar

to

block

walk

Form Meaning

A construction is a form-meaning pair whose properties may not be strictly predictable from other constructions.

(Construction Grammar, Goldberg 1995)

Source

Path

GoalTrajector

Page 8: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Form-meaning mappings for language

Formphonological

cuesword orderintonationinflection

Meaningevent structure

sensorimotor control

attention/perspective

social goals...

Linguistic knowledge consists of form-meaning mappings:

Cafe

Page 9: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Constructions as maps between relations

Mover + Motion + Directionbefore(Motion, Direction)before(Mover, Motion)

“is” + Action + “ing”before(“is”, Action)suffix(Action, “ing”)

Mover + Motionbefore(Mover, Motion)

Form Meaning

ProgressiveActionaspect(Action, ongoing)

MotionEventmover(Motion, Mover)

DirectedMotionEventdirection(Motion, Direction)mover(Motion, Mover)

Complex constructions are mappings between relations in form and relations in meaning.

Page 10: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Embodied Construction Grammar

• Embodied representations– active perceptual and motor schemas

– situational and discourse context

• Construction Grammar– Linguistic units relate form and meaning/function.

– Both constituency and (lexical) dependencies allowed.

• Constraint-based (Unification)– based on feature structures (as in HPSG)

– Diverse factors can flexibly interact.

Page 11: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

schema Containerroles

interiorexteriorportalboundary

Representing image schemas

Interior

Exterior

Boundary

PortalSource

Path

GoalTrajector

These are abstractions over sensorimotor experiences.

schema Source-Path-Goalroles

sourcepathgoaltrajector

schema name

role name

Page 12: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Inference and Conceptual Schemas

• Hypothesis: – Linguistic input is converted into a mental simulation based on bodily-

grounded structures.

• Components:– Semantic schemas

• image schemas and executing schemas are abstractions over neurally grounded perceptual and motor representations

– Linguistic units • lexical and phrasal construction representations invoke schemas, in part

through metaphor

• Inference links these structures and provides parameters for a simulation engine

Page 13: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Embodied Construction GrammarECG

(Formalizing Cognitive Linguisitcs)

1. Linguistic Analysis

2. Computational Implementationa. Test Grammars

b. Applied Projects – Question Answering

3. Map to Connectionist Models, Brain

4. Models of Grammar Acquisition

Page 14: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

ECG Structures

• Schemas– image schemas, force-dynamic schemas, executing

schemas, frames…

• Constructions– lexical, grammatical, morphological, gestural…

• Maps– metaphor, metonymy, mental space maps…

• Spaces– discourse, hypothetical, counterfactual…

Page 15: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

ECG Schemas

schema <name> subcase of <schema> evokes <schema> as

<local name> roles < local role >: <role restriction> constraints <role> ↔ <role> <role> <value> <predicate>

schema Hypotenuse subcase of Line-Segment

evokes Right-Tri as rt

roles

{lower-left: Point}

{upper-right: Point}

constraints

self ↔ rt.long-side

Page 16: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Source-Path-Goal; Container

schema SPG

subcase of TrajLandmark

roles

source: Place

path: Directed–Curve

goal: Place

{trajector: Entity}

{landmark: Bounded-

Region}

schema Container

roles

interior: Bounded-Region boundary: Curve portal: Bounded-Region

Page 17: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Referent Descriptor Schemas

schema RD

roles

category

gender

count

specificty

resolved Ref

modifications

schema RD5 // Eve

roles

HumanSchema

Female

one

Known

Eve Sweetser

none

Page 18: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

ECG Constructions

construction <name> subcase of <construction> constituents <name>:<construction> form constraints <name> before/meets

<name> meaning: constraints // same as for schemas

construction SpatialPP constituents prep: SpatialPreposition lm: NP form constraints prep meets lm meaning:

TrajectorLandmark constraints

selfm ↔ prep landmark ↔ lm.category

Page 19: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Into and The CXNsconstruction Into subcase

of SpatialPreposition

form: WordForm constraints

orth "into"

meaning: SPG

evokes Container as c constraints

landmark ↔ c

goal ↔ c.interior

construction The subcase of Determiner form:WordForm

constraints

orth "the"

meaning

evokes RD as rd

constraints rd.specificity “known”

Page 20: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Two Grammatical CXNsconstruction DetNoun

subcase of NP constituents

d:Determiner

n:Noun

form constraints

d before n

meaning constraints

selfm ↔ d.rd

category ↔ n

construction NPVP subcase of S constituents

subj: NP

vp: VP

form constraints

subj before vp

meaning constraints

profiled-participant ↔

subj

Page 21: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

construction ActiveSelfMotionPath subcase of ActiveMotionPath constituents

{v: verb}

{pp:SpatialPP}

form constraints

{v before pp}

meaning:SelfMotionPathEvent

constraints {spg ↔ pp} {profiled-participant ↔ mover} {profiled-process ↔ motion} {profiled-process ↔ v}

Construction WalkedVerb

subcase of PastPerfectiveVerb form constraints orth "walked" meaning:WalkAction 

Page 22: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Combined score determines best-fit

• Syntactic Fit:– Constituency relations– Combine with preferences on non-local elements– Conditioned on syntactic context

• Antecedent Fit:– Ability to find referents in the context– Conditioned on syntax match, feature agreement

• Semantic Fit:– Semantic bindings for frame roles– Frame roles’ fillers are scored

Page 23: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

0Eve1walked2into3the4house5

Constructs--------------NPVP[0] (0,5)Eve[3] (0,1)ActiveSelfMotionPath

[2] (1,5)WalkedVerb[57] (1,2)SpatialPP[56] (2,5)Into[174] (2,3)DetNoun[173] (3,5)The[204] (3,4)House[205] (4,5)

Schema Instances

-------------------

SelfMotionPathEvent[1]

HouseSchema[66]

WalkAction[60]

Person[4]

SPG[58]

RD[177] ~ house

RD[5]~ Eve

Page 24: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Unification chains and their fillers

SelfMotionPathEvent[1].mover

SPG[58].trajector

WalkAction[60].walker

RD[5].resolved-ref

RD[5].category

Filler: Person4

 

 

SpatialPP[56].m

Into[174].m

SelfMotionPathEvent[1].spg

Filler: SPG58

 

SelfMotionPathEvent[1]

.landmark

House[205].m

RD[177].category

SPG[58].landmark

Filler:HouseSchema66

 

 

WalkedVerb[57].m

WalkAction[60].routine

WalkAction[60].gait

SelfMotionPathEvent[1]

.motion

Filler:WalkAction60

Page 25: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Summary: ECG

• Linguistic constructions are tied to a model of simulated action and perception

• Embedded in a theory of language processing– Constrains theory to be usable– Frees structures to be just structures, used in processing

• Precise, computationally usable formalism– Practical computational applications, like MT and NLU– Testing of functionality, e.g. language learning

• A shared theory and formalism for different cognitive mechanisms– Constructions, metaphor, mental spaces, etc.

Page 26: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Embodied Compositional Semantics

after

Ellen [email protected]

Page 27: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Questions

• What is the nature of compositionality in the Neural Theory of Language?

• How can it be best represented using Embodied Construction Grammar?

Page 28: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Examples

• He bit the apple

• He was bitten (by a toddler)

• He bit into the apple

• His white teeth bit into the apple.

• He shattered the window

• The window was shattered

• The window shattered

Page 29: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Compositionality

• Put the parts together to create the meaning of the whole.

• Questions:– what is the nature of the parts?– How and why do they combine with one another?– What meaning is associated with this composition?

Page 30: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Short answers

• Parts = constructions, schemas

• Combination = binding, unification

• Meaning of the whole = simulation of unified parts

Page 31: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Simulation parameters

• Constructions unify to create semantic specification that supports a simulation

• Two types of simulation parameters for event descriptions:– Event content– Event construal

Page 32: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Summary

• Parts = constructions, schemas

• Combination = binding, unification

• Meaning of the whole = simulation of the combined parts

Page 33: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

First example

• He bit the apple.

Page 34: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

schema MotorControl subcase of Process roles Actor ↔ Protagonist

Effector Effort

Routine constraints Actor ← animate

Schemas

Page 35: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

schema ForceApplication subcase of MotorControl evokes ForceTransfer as FT roles

Actor ↔ FT.Supplier ↔ Protagonist Acted Upon↔ FT.Recipient Effector

Routine Effort ↔ FT.Force.amount

schema ForceTransfer evokes Conact as C roles

Supplier ↔ C.entity1 Recipient ↔ C.entity2 Force

schema MotorControl subcase of Process roles Actor ↔ Protagonist

Effector Effort

Routine constraints Actor ← animate

schema Contact subcase of SpatialRelation roles Entity1: entity Entity2: entity

Page 36: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Schema networks 

MotorControl

Motion

SPG

EffectorMotion

EffectorMotionPath

ForceTransfer

ForceApplication

ContactSpatiallyDirectedAction

CauseEffect

Contact

Agentive Impact

SelfMotion

SelfMotionPath

MotionPath

Page 37: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Construction BITE1 subcase of Verb form: bite meaning: ForceApplication constraints: Effector ← teeth Routine ← bite // close mouth 

Verb Constructions

schema ForceApplication subcase of MotorControl evokes ForceTransfer as FT roles Actor ↔ FT.Supplier ↔ Protagonist Acted Upon ↔ FT.Recipient Effector Routine Effort ↔ FT.Force.amount

Page 38: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Verb Constructions

schema ForceApplication subcase of MotorControl

schema Agentive Impact subcase of ForceApplication

cxn BITE meaning: ForceApplication

schema MotorControl

cxn GRASP meaning: ForceApplicationcxn PUSH meaning: ForceApplicationcxn SLAP meaning: AgentiveImpactcxn KICK meaning: AgentiveImpactcxn HIT meaning: AgentiveImpact

Page 39: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure Construction

construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: VF before NPF

meaning: CauseEffect evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant

FA ↔ Vm

Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NPm

Page 40: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure Construction

construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: VF before NPF

meaning: CauseEffect evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant

FA ↔ Vm

Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NPm

Page 41: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

CauseEffect schema

schema CauseEffect subcase of ForceApplication; Process roles

Causer ↔ Actor Affected ↔ ActedUpon ↔ Process.Protagonist Instrument ↔ Effector

Page 42: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

MotorControl

Motion

SPG

EffectorMotion

EffectorMotionPath

ForceTransfer

ForceApplication

ContactSpatiallyDirectedAction

CauseEffect

Contact

SelfMotion

SelfMotionPath

MotionPath

Agentive Impact

Process

Schema Network

Page 43: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure Construction

construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: VF before NPF

meaning: CauseEffect evokes: EventDescriptor as ED; ForceApplication as FA constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant

FA ↔ Vm

Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NPm

Page 44: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

MotorControl

Motion

SPG

EffectorMotion

EffectorMotionPath

ForceTransfer

ForceApplication

ContactSpatiallyDirectedAction

CauseEffect

Contact

SelfMotion

SelfMotionPath

MotionPath

Agentive Impact

Process

Schema Network

Page 45: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Important points

Compositionality does not require that each component contain different information.

Shared semantic structure is not viewed as an undesirable redundancy

Page 46: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure Construction

construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: VF before NPF

meaning: CauseEffect evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant FA ↔ Vm

Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NPm

Page 47: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

 

schema EventDescriptor

roles

EventType: Process

ProfiledProcess: Process

ProfiledParticipant: Entity

ProfiledState(s): State

SpatialSetting

TemporalSetting  

Event Descriptor schema

 

Page 48: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure Construction

Construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: VF before NPF

meaning: CauseEffect evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant FA ↔ Vm

Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NPm

Page 49: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

construction NPVP1 constituents: Subj: NP VP : VPform Constraints Subj f before VPf

meaning: EventDescriptor ProfiledParticipant ↔ Subjm   

Bindings with other cxnsconstruction ActiveTransitiveAction2 subcase of VP constituents: V ; NP form: VF before NPF

meaning: CauseEffect evokes; EventDescriptor as ED constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant

Affected ↔ NPm

Page 50: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Construction NPVP1 constituents: Subj: NP VP : VPform constraints Subj f before VPf

meaning: EventDescriptor ProfiledParticipant ↔ Subjm   

Bindings with other cxnsconstruction ActiveTransitiveAction2 subcase of VP constituents: V ; NP form: VF before NPF

meaning: CauseEffect evokes; EventDescriptor as ED constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant

Affected ↔ NPm

schema EventDescriptor roles EventType ProfiledProcess ProfiledParticipant ProfiledState(s) SpatialSetting TemporalSetting

Page 51: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Bindings with other cxns

schema EventDescriptor roles EventType ProfiledProcess ProfiledParticipant ProfiledState(s) SpatialSetting TemporalSetting

construction NPVP1 constituents: Subj: NP VP : VPform Constraints Subj f before VPf

meaning: EventDescriptor ProfiledParticipant ↔ Subjm   

construction ActiveTransitiveAction2 subcase of VP constituents: V ; NP form: VF before NPF

meaning: CauseEffect evokes; EventDescriptor as ED constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant

Affected ↔ NPm

Page 52: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Unification

CauseEffect causer affected

ForceApplication actor actedupon

EventDescriptor EventType ProfiledProcess ProfiledParticipant

BITE

TransitiveAction2

HE

NP1

NPVP1

THE APPLE

NP2ReferentDescriptor

ReferentDescriptor

Meaning Constructions

Page 53: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Unification

CauseEffect causer affected

ForceApplication actor actedupon

EventDescriptor EventType ProfiledProcess ProfiledParticipant

BITE

TransitiveAction2

HE

NP1

NPVP1

THE APPLE

NP2ReferentDescriptor

ReferentDescriptor resolved referent

Meaning Constructions

Page 54: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Unification

CauseEffect causer affected

ForceApplication actor actedupon

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

BITE

TransitiveAction2 Verb

HE

NP1

NPVP1

THE APPLE

NP2ReferentDescriptor

ReferentDescriptor resolved referent

Meaning Constructions

Page 55: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Unification

CauseEffect causer affected

ForceApplication actor actedupon

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

BITE

TransitiveAction2

HE

NP1

NPVP1 subj

THE APPLE

NP2ReferentDescriptor

ReferentDescriptor

Meaning Constructions

Page 56: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Unification

CauseEffect causer affected

ForceApplication actor actedupon

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

BITE

TransitiveAction2 NP

HE

NP1

NPVP1

THE APPLE

NP2ReferentDescriptor

ReferentDescriptor

Meaning Constructions

Page 57: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Semantic SpecificationHe bit the apple

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

CauseEffect causer affected

ForceApplication actor actedupon routine bite effector teeth

RD55category

Person

Apple

RD27category

Page 58: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Process

Simulation - He bit the apple

CauseEffect

ForceApplication

Protagonist = Causer ↔ Actor

Protagonist = Affected ↔ ActedUpon

Page 59: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Process

Simulation - He bit the apple

CauseEffect

ForceApplication

Protagonist = Causer ↔ Actor

Protagonist = Affected ↔ ActedUpon

Page 60: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Passive voice

He was bitten (by a toddler)

Page 61: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure ConstructionHe was bitten (by a toddler)

construction PassiveTransitiveAction2 subcase of VP constituents: V : PassiveVerb (PP: agentivePP) form constraints: VF before PPF

meaning: CauseEffectAction evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Affected ↔ ED.ProfiledParticipant FA ↔ Vm

Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Causer ↔ PP.NPm

Page 62: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Semantic SpecificationHe was bitten (by a toddler)

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

CauseEffect causer affected

ForceApplication actor actedupon routine bite effector teeth

RD48category

Person

Person

RD27category

Page 63: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Effect = Process

Simulation - He was bitten (by a toddler)

CauseEffect

Action = BiteProtagonist = Causer ↔ Actor

Protagonist = Affected ↔ ActedUpon

Page 64: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Variations on a theme

• He shattered the window

• The window was shattered

• The window shattered

Page 65: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Construction SHATTER1 subcase of Verb form: shatter meaning: StateChange constraints: Initial :: Undergoer.state ← whole Final :: Undergoer.state ← shards 

Verb Construction -- shatter

schema StateChange subcase of Process roles Undergoer ↔ Protagonist

Page 66: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure ConstructionHe shattered the window

construction ActiveTransitiveAction3 subcase of VP constituents: V : verb NP: NP form constraints: VF before NPF

meaning: CauseEffect evokes: EventDescriptor as ED; StateChange as SC constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Causer ↔ ED.ProfiledParticipant

SC ↔ Vm

Affected ↔ SC.Undergoer Affected ↔ NPm

Page 67: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Semantic SpecificationHe shattered the window

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

CauseEffect causer affected

StateChange Undergoer state “wholeness”

RD189category

Person

Window

RD27category

Page 68: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Process

Simulation - He shattered the window

CauseEffect

Action Protagonist = Causer

Protagonist = Affected ↔ Undergoer

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Argument Structure ConstructionThe window was shattered

construction PassiveTransitiveAction3 subcase of VP constituents: V : PassiveVerb (PP: agentivePP) form constraints: VF before NPF

meaning: CauseEffect evokes: EventDescriptor as ED; StateChange as SC constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Affected ↔ ED.ProfiledParticipant

SC ↔ Vm

Affected ↔ SC.Undergoer Causer ↔ PP.NPm

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Semantic SpecificationThe window was shattered

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

CauseEffect causer affected

StateChange Undergoer state “wholeness”

RD175category

Window

Page 71: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Process

Simulation - The window was shattered

CauseEffect

Action Protagonist = Causer

Protagonist = Affected ↔ Undergoer

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Argument Structure ConstructionThe window shattered

construction ActiveIntransitiveAction1 subcase of VP constituents: V : verb form meaning: Process evokes: EventDescriptor as ED; StateChange as SC constraints: {Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Protagonist ↔ ED.ProfiledParticipant

SC ↔ Vm

Protagonist ↔ SC.Undergoer

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Semantic SpecificationThe window shattered

EventDescriptor eventtype ProfiledProcess ProfiledParticipant

Process protagonist

StateChange Undergoer state “wholeness”

RD177categoryWindow

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Process

Simulation - The window shattered

Process

Protagonist = Undergoer

Page 75: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Some more variations on a theme

• He bit the apple

• He bit into the apple

• His white teeth bit into the apple.

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Argument Structure ConstructionHe bit into the apple

construction ActiveEffectorMotionPath2 subcase of VP constituents: V : verb PP: Spatial-PP form constraints:

VF before PPF

meaning: EffectorMotionPath evokes; EventDescriptor as ED; ForceApplication as FA constraints:

{Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Actor ↔ ED.ProfiledParticipant FA ↔ Vm

Actor ↔ FA.ActorEffector ↔ FA.Effector // INITarget ↔ FA.ActedUpon SPG ↔ PPm

Target ↔ PPm .Prep.LM

 

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Schema

schema EffectorMotionPath subcase of EffectorMotion subcase of SPG // or evokes SPG roles Actor ↔ MotorControl.protagonist

Effector ↔ SPG.Tr ↔ M.Mover ↔ Motion.protagonistTarget ↔ SPG.Lm

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MotorControl

Motion

SPG

EffectorMotion

EffectorMotionPath

ForceTransfer

ForceApplication

ContactSpatiallyDirectedAction

CauseEffect

Contact

SelfMotion

SelfMotionPath

MotionPath

Agentive Impact

Process

Schema Network

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Argument Structure Construction He bit into the apple

construction ActiveEffectorMotionPath2 subcase of VP constituents: V : verb PP: Spatial-PP form constraints:

VF before PPF

meaning: EffectorMotionPath evokes: EventDescriptor as ED; ForceApplication as FA constraints:

{Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Actor ↔ ED.ProfiledParticipant FA ↔ Vm

Actor ↔ FA.ActorEffector ↔ FA.Effector // INITarget ↔ FA.ActedUpon SPG ↔ PPm

Target ↔ PPm .Prep.LM

 

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EffectorMotionPath

Action

Source

Path Goal

Effector Motion

Protagonist = Actor

Protagonist = Effector

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Argument Structure Construction He bit into the apple

construction ActiveEffectorMotionPath2 subcase of VP constituents: V : verb PP: Spatial-PP form constraints:

VF before PPF

meaning: EffectorMotionPath evokes; EventDescriptor as ED; ForceApplication as FA constraints:

{Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Actor ↔ ED.ProfiledParticipant FA ↔ Vm

Actor ↔ FA.ActorEffector ↔ FA.Effector // INITarget ↔ FA.ActedUpon SPG ↔ PPm

Target ↔ PPm .Prep.LM

 

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Simulation: He bit into the apple

Action

Source

Path Goal

Effector Motion

Protagonist = Actor

Protagonist = Effector

Page 83: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Argument Structure ConstructionHis white teeth bit into the apple

construction ActiveEffectorMotionPath3 subcase of VP constituents: V : verb PP: Spatial-PP form constraints:

VF before PPF

meaning: EffectorMotionPath evokes; EventDescriptor as ED; ForceApplication as FA constraints:

{Selfm ↔ ED.EventType}

{Vm ↔ ED.ProfiledProcess}

Effector ↔ ED.ProfiledParticipant FA ↔ Vm

Actor ↔ FA.Actor // INIEffector ↔ FA.EffectorTarget ↔ FA.ActedUpon SPG ↔ PPm

Target ↔ PPm .Prep.LM

 

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Simulation: His white teeth bit into the apple

Action

Source

Path Goal

Effector Motion

Protagonist = Actor

Protagonist = Effector

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Non-agentive biting

• He landed on his feet, hitting the narrow pavement outside the yard with such jarring impact that his teeth bit into the edge of his tongue. [BNC]

• The studs bit into Trent's hand. [BNC]

• His chest burned savagely as the ropes bit into his skin. [BNC]

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MotorControl

Motion

SPG

EffectorMotion

EffectorMotionPath

ForceTransfer

ForceApplication

ContactSpatiallyDirectedAction

CauseEffect

Contact

SelfMotion

SelfMotionPath

MotionPath

Agentive Impact

Process

Schema Network

Page 87: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Simulation: His teeth bit his tongue

Source

Path Goal

MotionProtagonist = Mover

Page 88: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Summary

• Small set of constructions and schemas

• Composed in different ways

• Unification produces specification of parameters of simulation

• Sentence understanding is simulation

• Different meanings = different simulations

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Concluding Remarks

• Complexity

• Simulation

Page 90: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Concluding Remarks

• Complexity• Simulation• Language understanding is simulation• Simulation involves activation of

conceptual structures• Simulation specifications should include:

– which conceptual structures to activate– how these structures should be activated

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Extra slides follow:

Page 92: What does language do? “Harry walked to the cafe.” “Harry walked into the cafe.” A sentence can evoke an imagined scene and resulting inferences : CAFE

Prototypes and extensions?

CauseMotion Path:

• He threw the ball across the room

• He kicked the ball over the table

• He sneezed the napkin off the table

• [He coughed the water out of his lungs]

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Key points

• In prototypical verb-argument structure construction combinations, verb meaning is very similar to argument structure meaning.

• Verbs whose meaning partially overlaps that of a given argument structure constructions may also co-occur with that argument structure construction

• These less prototypical combinations may motivate extensions to the central argument structure constructions