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

Language understanding Interpretation (Utterance, Situation) Linguistic knowledge Conceptual knowledge Analysis

Language understanding: analysis & simulation “Harry walked to the cafe.” SchemaTrajector Goal walkHarrycafe Cafe Lexicon Constructicon General Knowledge Belief State Analysis Process Semantic Specification Utterance Simulation

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.

Phonetics Semantics Pragmatics Morphology Syntax Traditional Levels of Analysis

Phonetics Semantics Pragmatics Morphology Syntax “Harry walked into the cafe.” Utterance

Construction Grammar to block walk FormMeaning A construction is a form-meaning pair whose properties may not be strictly predictable from other constructions. (Construction Grammar, Goldberg 1995) Source Path Goal Trajector

Form-meaning mappings for language Form phonological cues word order intonation inflection Meaning event structure sensorimotor control attention/perspective social goals... Linguistic knowledge consists of form-meaning mappings : Cafe

Constructions as maps between relations Mover + Motion + Direction before(Motion, Direction) before(Mover, Motion) “is” + Action + “ing” before(“is”, Action) suffix(Action, “ing”) Mover + Motion before(Mover, Motion) FormMeaning ProgressiveAction aspect(Action, ongoing) MotionEvent mover(Motion, Mover) DirectedMotionEvent direction(Motion, Direction) mover(Motion, Mover) Complex constructions are mappings between relations in form and relations in meaning.

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.

schema Container roles interior exterior portal boundary Representing image schemas Interior Exterior Boundary Portal Source Path Goal Trajector These are abstractions over sensorimotor experiences. schema Source-Path-Goal roles source path goal trajector schema name role name

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

Embodied Construction Grammar ECG (Formalizing Cognitive Linguisitcs) 1.Linguistic Analysis 2.Computational Implementation a.Test Grammars b.Applied Projects – Question Answering 3.Map to Connectionist Models, Brain 4.Models of Grammar Acquisition

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…

ECG Schemas schema subcase of evokes as roles : constraints ↔  schema Hypotenuse subcase of Line-Segment evokes Right-Tri as rt roles {lower-left: Point} {upper-right: Point} constraints self ↔ rt.long-side

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

Referent Descriptor Schemas schema RD roles category gender count specificty resolved Ref modifications schema RD5 // Eve roles HumanSchema Female one Known Eve Sweetser none

ECG Constructions construction subcase of constituents : form constraints before/meets meaning: constraints // same as for schemas construction SpatialPP constituents prep: SpatialPreposition lm: NP form constraints prep meets lm meaning: TrajectorLandmark constraints self m ↔ prep landmark ↔ lm.category

Into and The CXNs construction 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”

Two Grammatical CXNs construction DetNoun subcase of NP constituents d:Determiner n:Noun form constraints d before n meaning constraints self m ↔ d.rd category ↔ n construction NPVP subcase of S constituents subj: NP vp: VP form constraints subj before vp meaning constraints profiled-participant ↔ subj

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

Competition-based analyzer An analysis is made up of: –A constructional tree –A semantic specification –A set of resolutions Bill gaveMarythe book MaryBill Ref-Exp Give A-GIVE-B-X subj vobj1 obj2 book01@Man@WomanGive-Action @Book giver recipient theme Johno Bryant

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

0 Eve 1 walked 2 into 3 the 4 house 5 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

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

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.

A Best-Fit Approach for Productive Analysis of Omitted Arguments Eva Mok & John Bryant University of California, Berkeley International Computer Science Institute

Simplify grammar by exploiting the language understanding process Omission of arguments in Mandarin Chinese Construction grammar framework Model of language understanding Our best-fit approach

Mother (I) give you this (a toy). CHILDES Beijing Corpus (Tardiff, 1993; Tardiff, 1996) ma1+magei3ni3zhei4+ge mothergive2PSthis+CLS You give auntie [the peach]. Oh (go on)! You give [auntie] [that]. Productive Argument Omission (in Mandarin ) 1 2 3 ni3gei3yi2 2PSgiveauntie aoni3gei3ya EMP2PSgiveEMP 4 gei3 give [I] give [you] [some peach].

Arguments are omitted with different probabilities All arguments omitted: 30.6%No arguments omitted: 6.1%

Construction grammar approach Kay & Fillmore 1999; Goldberg 1995 Grammaticality: form and function Basic unit of analysis: construction, i.e. a pairing of form and meaning constraints Not purely lexically compositional Implies early use of semantics in processing Embodied Construction Grammar (ECG) (Bergen & Chang, 2005)

Problem: Proliferation of constructions SubjVerbObj1Obj2 ↓↓↓↓ GiverTransferRecipientTheme VerbObj1Obj2 ↓↓↓ TransferRecipientTheme … SubjVerbObj2 ↓↓↓ GiverTransferTheme SubjVerbObj1 ↓↓↓ GiverTransferRecipient

If the analysis process is smart, then... The grammar needs only state one construction Omission of constituents is flexibly allowed The analysis process figures out what was omitted SubjVerbObj1Obj2 ↓↓↓↓ GiverTransferRecipientTheme

Best-fit analysis process takes burden off the grammar representation Constructions Simulation Utterance Discourse & Situational Context Semantic Specification: image schemas, frames, action schemas Analyzer: incremental, competition-based, psycholinguistically plausible

Competition-based analyzer finds the best analysis An analysis is made up of: –A constructional tree –A set of resolutions –A semantic specification The best fit has the highest combined score

Combined score that 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 syntactic information, feature agreement Semantic Fit: –Semantic bindings for frame roles –Frame roles’ fillers are scored

Analyzing ni3 gei3 yi2 (You give auntie) Syntactic Fit: –P(Theme omitted | ditransitive cxn) = 0.65 –P(Recipient omitted | ditransitive cxn) = 0.42 Two of the competing analyses: ni3gei3yi2omitted ↓↓↓↓ GiverTransferRecipientTheme ni3gei3omittedyi2 ↓↓↓↓ GiverTransferRecipientTheme (1-0.78)*(1-0.42)*0.65 = 0.08(1-0.78)*(1-0.65)*0.42 = 0.03

Using frame and lexical information to restrict type of reference Lexical Unit gei3 Giver (DNI) Recipient (DNI) Theme (DNI) The Transfer Frame Giver Recipient Theme Manner Means Place Purpose Reason Time

Can the omitted argument be recovered from context? Antecedent Fit: ni3gei3yi2omitted ↓↓↓↓ GiverTransferRecipientTheme ni3gei3omittedyi2 ↓↓↓↓ GiverTransferRecipientTheme Discourse & Situational Context childmother peachauntie table ?

How good of a theme is a peach? How about an aunt? The Transfer Frame Giver (usually animate) Recipient (usually animate) Theme (usually inanimate) ni3gei3yi2omitted ↓↓↓↓ GiverTransferRecipientTheme ni3gei3omittedyi2 ↓↓↓↓ GiverTransferRecipientTheme Semantic Fit:

The argument omission patterns shown earlier can be covered with just ONE construction Each cxn is annotated with probabilities of omission Language-specific default probability can be set SubjVerbObj1Obj2 ↓↓↓↓ GiverTransferRecipientTheme 0.780.420.65 P(omitted|cxn):

Leverage process to simplify representation The processing model is complementary to the theory of grammar By using a competition-based analysis process, we can: –Find the best-fit analysis with respect to constituency structure, context, and semantics –Eliminate the need to enumerate allowable patterns of argument omission in grammar This is currently being applied in models of language understanding and grammar learning.

Best-fit example with theme omitted SubjVerbObj1Obj2 ↓↓↓↓ GiverTransferRecipien t Theme You give auntie [the peach]. 2 Verb ↓ Transfer local? omitted? local Subj ↓ Giver omitted local? omitted? local Obj1 ↓ Recipien t Obj2 ↓ Theme ni3gei3yi2 2PSgiveauntie

Lexical Unit gei3 Giver Recipient Theme How to recover the omitted argument, in this case the peach? The Transfer Frame Giver Recipient Theme Manner Means Place Purpose Reason Time (DNI) Discourse & Situational Context child mother auntie peach table omitted Obj2 ↓ Theme

Best-fit example with theme omitted Oh (go on)! You give [auntie] [that]. 3 Verb ↓ Transfer local? omitted? local omitted Subj ↓ Giver omitted local? omitted? local Obj1 ↓ Recipient Obj2 ↓ Theme aoni3gei3ya EMP2PSgiveEMP

Lexical Unit gei3 Giver Recipient Theme How to recover the omitted argument, in this case the aunt and the peach? The Transfer Frame Giver Recipient Theme Manner Means Place Purpose Reason Time (DNI) Discourse & Situational Context child mother auntie peach table omitted Obj2 ↓ Theme omitted Obj1 ↓ Recipient

Embodied Compositional Semantics after Ellen Dodge edodge@berkeley.edu

Questions What is the nature of compositionality in the Neural Theory of Language? How can it be best represented using Embodied Construction Grammar?

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

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?

Short answers Parts = constructions, schemas Combination = binding, unification Meaning of the whole = simulation of unified parts

Constructions Construction Grammar Constructions are form-meaning pairings A given utterance instantiates many different constructions Embodied Construction Grammar Construction meaning is represented using schemas Meaning is embodied

Key assumptions of NTL Language understanding is simulation Simulation involves activation of neural structures

Comments Language understanding Understanding process is dynamic “Redundancy” is okay

Conceptual structure Embodied Schematic (Potentially) language-independent Highly interconnected

Simulation parameters Constructions unify to create semantic specification that supports a simulation Two types of simulation parameters for event descriptions: –Event content –Event construal

Putting the parts together Bindings Unification

“Pre-existing” structure Cxn schema Cxn schema

Unification Cxn schema Cxn schema

Summary Parts = constructions, schemas Combination = binding, unification Meaning of the whole = simulation of the combined parts

First example He bit the apple.

schema MotorControl subcase of Process roles Actor ↔ Protagonist Effector Effort Routine constraints Actor ← animate Schemas

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

Schema networks MotorControl Motion SPG Effector Motion Effector MotionPath ForceTransfer ForceApplication Contact SpatiallyDirectedAction CauseEffect Contact Agentive Impact SelfMotion Path MotionPath

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

Verb Constructions schema ForceApplication subcase of MotorControl schema Agentive Impact subcase of ForceApplication cxn BITE meaning: ForceApplication schema MotorControl cxn GRASP meaning: ForceApplication cxn PUSH meaning: ForceApplication cxn SLAP meaning: AgentiveImpact cxn KICK meaning: AgentiveImpact cxn HIT meaning: AgentiveImpact

Argument Structure Construction construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: V F before NP F meaning: CauseEffect evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant FA ↔ V m Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NP m

CauseEffect schema schema CauseEffect subcase of ForceApplication; Process roles Causer ↔ Actor Affected ↔ ActedUpon ↔ Process.Protagonist Instrument ↔ Effector

MotorControl Motion SPG Effector Motion Effector MotionPath ForceTransfer ForceApplication Contact SpatiallyDirectedAction CauseEffect Contact SelfMotion Path MotionPath Agentive Impact Process Schema Network

Argument Structure Construction construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: V F before NP F meaning: CauseEffect evokes: EventDescriptor as ED; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant FA ↔ V m Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NP m

Important points  Compositionality does not require that each component contain different information.  Shared semantic structure is not viewed as an undesirable redundancy

Argument Structure Construction construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: V F before NP F meaning: CauseEffect evokes; EventDescriptor as ED ; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant FA ↔ V m Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NP m

schema EventDescriptor roles EventType: Process ProfiledProcess: Process ProfiledParticipant: Entity ProfiledState(s): State SpatialSetting TemporalSetting Event Descriptor schema

Argument Structure Construction Construction ActiveTransitiveAction2 subcase of VP constituents: V : verb NP: NP form constraints: V F before NP F meaning: CauseEffect evokes; EventDescriptor as ED ; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant FA ↔ V m Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Affected ↔ NP m

construction NPVP1 constituents: Subj: NP VP : VP form Constraints Subj f before VP f meaning: EventDescriptor ProfiledParticipant ↔ Subj m Bindings with other cxns construction ActiveTransitiveAction2 subcase of VP constituents: V ; NP form: V F before NP F meaning: CauseEffect evokes; EventDescriptor as ED constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant Affected ↔ NP m

Construction NPVP1 constituents: Subj: NP VP : VP form constraints Subj f before VP f meaning: EventDescriptor ProfiledParticipant ↔ Subj m Bindings with other cxns construction ActiveTransitiveAction2 subcase of VP constituents: V ; NP form: V F before NP F meaning: CauseEffect evokes; EventDescriptor as ED constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant Affected ↔ NP m schema EventDescriptor roles EventType ProfiledProcess ProfiledParticipant ProfiledState(s) SpatialSetting TemporalSetting

Bindings with other cxns schema EventDescriptor roles EventType ProfiledProcess ProfiledParticipant ProfiledState(s) SpatialSetting TemporalSetting construction NPVP1 constituents: Subj: NP VP : VP form Constraints Subj f before VP f meaning: EventDescriptor ProfiledParticipant ↔ Subj m construction ActiveTransitiveAction2 subcase of VP constituents: V ; NP form: V F before NP F meaning: CauseEffect evokes; EventDescriptor as ED constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant Affected ↔ NP m

Unification CauseEffect causer affected ForceApplication actor actedupon EventDescriptor EventType ProfiledProcess ProfiledParticipant BITE TransitiveAction2 HE NP1 NPVP1 THEAPPLE NP2 ReferentDescriptor ReferentDescriptor MeaningConstructions

Unification CauseEffect causer affected ForceApplication actor actedupon EventDescriptor EventType ProfiledProcess ProfiledParticipant BITE TransitiveAction2 HE NP1 NPVP1 THEAPPLE NP2 ReferentDescriptor ReferentDescriptor resolved referent MeaningConstructions

Unification CauseEffect causer affected ForceApplication actor actedupon EventDescriptor eventtype ProfiledProcess ProfiledParticipant BITE TransitiveAction2 Verb HE NP1 NPVP1 THEAPPLE NP2 ReferentDescriptor ReferentDescriptor resolved referent MeaningConstructions

Unification CauseEffect causer affected ForceApplication actor actedupon EventDescriptor eventtype ProfiledProcess ProfiledParticipant BITE TransitiveAction2 HE NP1 NPVP1 subj THEAPPLE NP2 ReferentDescriptor ReferentDescriptor MeaningConstructions

Unification CauseEffect causer affected ForceApplication actor actedupon EventDescriptor eventtype ProfiledProcess ProfiledParticipant BITE TransitiveAction2 NP HE NP1 NPVP1 THEAPPLE NP2 ReferentDescriptor ReferentDescriptor MeaningConstructions

Semantic Specification He bit the apple EventDescriptor eventtype ProfiledProcess ProfiledParticipant CauseEffect causer affected ForceApplication actor actedupon routine  bite effector  teeth RD55 category Person Apple RD27 category

Process Simulation - He bit the apple CauseEffect ForceApplication Protagonist = Causer ↔ Actor Protagonist = Affected ↔ ActedUpon

Passive voice He was bitten (by a toddler)

Argument Structure Construction He was bitten (by a toddler) construction PassiveTransitiveAction2 subcase of VP constituents: V : PassiveVerb (PP: agentivePP) form constraints: V F before PP F meaning: CauseEffectAction evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Affected ↔ ED.ProfiledParticipant FA ↔ V m Causer ↔ FA.Actor Affected ↔ FA.ActedUpon Causer ↔ PP.NP m

Semantic Specification He was bitten (by a toddler) EventDescriptor eventtype ProfiledProcess ProfiledParticipant CauseEffect causer affected ForceApplication actor actedupon routine  bite effector  teeth RD48 category Person Person RD27 category

Effect = Process Simulation - He was bitten (by a toddler) CauseEffect Action = Bite Protagonist = Causer ↔ Actor Protagonist = Affected ↔ ActedUpon

Variations on a theme He shattered the window The window was shattered The window shattered

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

Argument Structure Construction He shattered the window construction ActiveTransitiveAction3 subcase of VP constituents: V : verb NP: NP form constraints: V F before NP F meaning: CauseEffect evokes: EventDescriptor as ED; StateChange as SC constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Causer ↔ ED.ProfiledParticipant SC ↔ V m Affected ↔ SC.Undergoer Affected ↔ NP m

Semantic Specification He shattered the window EventDescriptor eventtype ProfiledProcess ProfiledParticipant CauseEffect causer affected StateChange Undergoer state  “wholeness” RD189 category Person Window RD27 category

Process Simulation - He shattered the window CauseEffect Action Protagonist = Causer Protagonist = Affected ↔ Undergoer

Argument Structure Construction The window was shattered construction PassiveTransitiveAction3 subcase of VP constituents: V : PassiveVerb (PP: agentivePP) form constraints: V F before NP F meaning: CauseEffect evokes: EventDescriptor as ED; StateChange as SC constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Affected ↔ ED.ProfiledParticipant SC ↔ V m Affected ↔ SC.Undergoer Causer ↔ PP.NP m

Semantic Specification The window was shattered EventDescriptor eventtype ProfiledProcess ProfiledParticipant CauseEffect causer affected StateChange Undergoer state  “wholeness” RD175 category Window

Process Simulation - The window was shattered CauseEffect Action Protagonist = Causer Protagonist = Affected ↔ Undergoer

Argument Structure Construction The window shattered construction ActiveIntransitiveAction1 subcase of VP constituents: V : verb form meaning: Process evokes: EventDescriptor as ED; StateChange as SC constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Protagonist ↔ ED.ProfiledParticipant SC ↔ V m Protagonist ↔ SC.Undergoer

Semantic Specification The window shattered EventDescriptor eventtype ProfiledProcess ProfiledParticipant Process protagonist StateChange Undergoer state  “wholeness” RD177 category Window

Process Simulation - The window shattered Process Protagonist = Undergoer

Some more variations on a theme He bit the apple He bit into the apple His white teeth bit into the apple.

Argument Structure Construction He bit into the apple construction ActiveEffectorMotionPath2 subcase of VP constituents: V : verb PP: Spatial-PP form constraints: V F before PP F meaning: EffectorMotionPath evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Actor ↔ ED.ProfiledParticipant FA ↔ V m Actor ↔ FA.Actor Effector ↔ FA.Effector // INI Target ↔ FA.ActedUpon SPG ↔ PP m Target ↔ PP m.Prep.LM

Schema schema EffectorMotionPath subcase of EffectorMotion subcase of SPG // or evokes SPG roles Actor ↔ MotorControl.protagonist Effector ↔ SPG.Tr ↔ M.Mover ↔ Motion.protagonist Target ↔ SPG.Lm

Argument Structure Construction He bit into the apple construction ActiveEffectorMotionPath2 subcase of VP constituents: V : verb PP: Spatial-PP form constraints: V F before PP F meaning: EffectorMotionPath evokes: EventDescriptor as ED; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Actor ↔ ED.ProfiledParticipant FA ↔ V m Actor ↔ FA.Actor Effector ↔ FA.Effector // INI Target ↔ FA.ActedUpon SPG ↔ PP m Target ↔ PP m.Prep.LM

EffectorMotionPath Action SourcePathGoal Effector Motion Protagonist = Actor Protagonist = Effector

Argument Structure Construction He bit into the apple construction ActiveEffectorMotionPath2 subcase of VP constituents: V : verb PP: Spatial-PP form constraints: V F before PP F meaning: EffectorMotionPath evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Actor ↔ ED.ProfiledParticipant FA ↔ V m Actor ↔ FA.Actor Effector ↔ FA.Effector // INI Target ↔ FA.ActedUpon SPG ↔ PP m Target ↔ PP m.Prep.LM

Simulation: He bit into the apple Action SourcePathGoal Effector Motion Protagonist = Actor Protagonist = Effector

Argument Structure Construction His white teeth bit into the apple construction ActiveEffectorMotionPath3 subcase of VP constituents: V : verb PP: Spatial-PP form constraints: V F before PP F meaning: EffectorMotionPath evokes; EventDescriptor as ED; ForceApplication as FA constraints: {Self m ↔ ED.EventType} {V m ↔ ED.ProfiledProcess} Effector ↔ ED.ProfiledParticipant FA ↔ V m Actor ↔ FA.Actor // INI Effector ↔ FA.Effector Target ↔ FA.ActedUpon SPG ↔ PP m Target ↔ PP m.Prep.LM

Simulation: His white teeth bit into the apple Action SourcePathGoal Effector Motion Protagonist = Actor Protagonist = Effector

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]

Simulation: His teeth bit his tongue SourcePathGoal Motion Protagonist = Mover

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

Concluding Remarks Complexity Simulation

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

Extra slides follow:

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]

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

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.

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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.

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Presentation on theme: "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."— Presentation transcript:

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