Getting From the Utterance to the SemSpec Johno Bryant Need a grammar formalism –Embodied Construction Grammar (Bergen & Chang 2002) Need new models for.

Getting From the Utterance to the SemSpec Johno Bryant Need a grammar formalism –Embodied Construction Grammar (Bergen & Chang 2002) Need new models for language analysis –Traditional methods too limited –Traditional methods also don’t get enough leverage out of the semantics.

Embodied Construction Grammar Semantic Freedom –Designed to be symbiotic with cognitive approaches to meaning –More expressive semantic operators than traditional grammar formalisms Form Freedom –Free word order, over-lapping constituency Precise enough to be implemented

Traditional Parsing Methods Fall Short PSG parsers too strict –Constructions not allowed to leave constituent order unspecified Traditional way of dealing with incomplete analyses is ad-hoc –Making sense of incomplete analyses is important when an application must deal with “ill-formed” input (For example, modeling language learning) Traditional unification grammar can’t handle ECG’s deep semantic operators.

Our Analyzer Replaces the FSMs used in traditional chunking (Abney 96) with much more powerful machines capable of backtracking called construction recognizers Arranges these recognizers into levels just like in Abney’s work But uses a chart to deal with ambiguity

Our Analyzer (cont’d) Uses specialized feature structures to deal with ECG’s novel semantic operators Supports a heuristic evaluation metric for finding the “right” analysis Puts partial analyses together when no complete analyses are available –The analyzer was designed under the assumption that the grammar won’t cover every meaningful utterance encountered by the system.

System Architecture Learner Semantic Chunker Semantic Integration Grammar/Utterance Chunk Chart Ranked Analyses

The Levels The analyzer puts the recognizer on the level assigned by the grammar writer. –Assigned level should be greater than or equal to the levels of the construction’s constituents. The analyzer runs all the recognizers on level 1, then level 2, etc. until no more levels. Recognizers on the same level can be mutually recursive.

Recognizers Each Construction is turned into a recognizer Recognizer = active representation –seeks form elements/constituents when initiated –Unites grammar and process - grammar isn’t just a static piece of knowledge in this model. Checks both form and semantic constraints –Contains an internal representation of both the semantics and the form –A graph data structure used to represent the form and a feature structure representation for the meaning.

Recognizer Example Path Patient ActionAgent Mary kicked the ball into the net. This is the initial Constituent Graph for caused-motion.

Recognizer Example Construct: Caused-Motion Constituent: Agent Constituent: Action Constituent: Patient Constituent: Path The initial constructional tree for the instance of Caused-Motion that we are trying to create.

Recognizer Example

processed Mary kicked the ball into the net. Path Patient ActionAgent A node filled with gray is removed.

Recognizer Example Construct: Caused-Motion Constituent: Action Constituent: Patient Constituent: Path RefExp: Mary Mary kicked the ball into the net.

Recognizer Example

processed Mary kicked the ball into the net. Path Patient ActionAgent

Recognizer Example Construct: Caused-Motion Verb: kicked Constituent: Patient Constituent: Path RefExp: Mary Mary kicked the ball into the net.

Recognizer Example

processed Mary kicked the ball into the net. Path Patient ActionAgent According to the Constituent Graph, The next constituent can either be the Patient or the Path.

Recognizer Example processed Mary kicked the ball into the net. Path Patient ActionAgent

Recognizer Example Construct: Caused-Motion Verb: kicked RefExp: Det Noun Constituent: Path RefExp: Mary Mary kicked the ball into the net. Noun Det

Recognizer Example

processed Mary kicked the ball into the net. Path Patient ActionAgent

Recognizer Example Construct: Caused-Motion Verb: kicked RefExp: Det Noun Spatial-Pred: Prep RefExp RefExp: Mary Mary kicked the ball into the net. Noun Det Noun DetPrep RefExp

Recognizer Example

Scene = Caused-Motion Agent = Mary Action = Kick Patient = Path.Trajector = The Ball Path = Into the net Path.Goal = The net After analyzing the sentence, the following identities are asserted in the resulting SemSpec: Resulting SemSpec

Progress The analyzer (as described so far) is already being put to use in Chang’s thesis work. –The levels are well-suited to incremental learning. –Syntactic robustness important for generating partial analyses with poor coverage It will also be used this semester for producing SemSpecs for Narayanan’s enactment engine. –Put the deep semantics towards parameterizing x- schemas

Chunking 0 1 2 3 4 5 6 7 8 9 the woman in the lab coat thought you were sleeping L0 D N P D N N V-tns Pron Aux V-ing L1 ____NP P_______NP VP NP ______VP L2 ____NP _________PP VP NP ______VP L3 ________________________S_____________S Cite/description

Construction Recognizers You want to put a cloth on your hand ? NP Form Meaning “you” [Addressee] Form Meaning D,N [Cloth num:sg] Form Meaning PP$,N [Hand num:sg poss:addr] Like Abney:Unlike Abney: One recognizer per rule Bottom up and level-based Check form and semantics More powerful/slower than FSMs

Chunk Chart Interface between chunking and structure merging Each edge is linked to its corresponding semantics. You want to put a cloth on your hand ?

Combining Partial Parses Prefer an analysis that spans the input utterance with the minimum number of chunks. When no spanning analysis exists, however, we still have a chart full of semantic chunks. The system tries to build a coherent analysis out of these semantics chunks. This is where structure merging comes in.

Structure Merging Closely related to abductive inferential mechanisms like abduction (Hobbs) Unify compatible structures (find fillers for frame roles) Intuition: Unify structures that would have been co- indexed had the missing construction been defined. There are many possible ways to merge structures. In fact, there are an exponential number of ways to merge structures (NP Hard). But using heuristics cuts down the search space.

Structure Merging Example Utterance:You used to hate to have the bib put on. [Addressee < Animate] Bib < Clothing num:sg givenness:def Caused-Motion-Action Agent: [Animate] Patient: [Entity] Path:On Before Merging: After Merging: Caused-Motion-Action Agent: [Addressee] Patient: Path:On Bib < Clothing num:sg givenness:def

Semantic Density Semantic density is a simple heuristic to choose between competing analyses. Density of an analysis = (filled roles) / (total roles) The system prefers higher density analyses because a higher density suggests that more frame roles are filled in than in competing analyses. Extremely simple / useful? but it certainly can be improved upon.

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.

Issues in Scaling up to Language Knowledge –Lexicon (FrameNet ) –Constructicon (ECG) –Maps (Metaphors, Metonymies) (MetaNet) –Conceptual Relations (Image Schemas, X-schemas) Computation –Representation (ECG) expressiveness, modularity, compositionality –Inference (Simulation Semantics) tractable, distributed, probabilistic concurrent, context- sensitive

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 Ellen Dodge edodge@berkeley.edu March 9, 2007

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

Outline Compositionality Neural Theory of Language and ECG –Assumptions –Overview Examples: –Representation of constructions and meaning –Simulation Concluding Remarks

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

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

Getting From the Utterance to the SemSpec Johno Bryant Need a grammar formalism –Embodied Construction Grammar (Bergen & Chang 2002) Need new models for.

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Getting From the Utterance to the SemSpec Johno Bryant Need a grammar formalism –Embodied Construction Grammar (Bergen & Chang 2002) Need new models for.

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Presentation on theme: "Getting From the Utterance to the SemSpec Johno Bryant Need a grammar formalism –Embodied Construction Grammar (Bergen & Chang 2002) Need new models for."— Presentation transcript:

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