Documentation

Linglib.Phenomena.Ellipsis.Studies.GinzburgCooper2004

Ginzburg & Cooper (2004): Clarification, Ellipsis, and Contextual Updates #

@cite{ginzburg-cooper-2004}

Formalization of the core running example from @cite{ginzburg-cooper-2004}:

A: "Did Bo leave?" B: "Bo?"

This study applies the KOS framework (DGB, IS, C-PARAMS, coercion operations) to derive both CE readings — clausal and constituent — from the same antecedent sign, and demonstrates the speaker/addressee IS asymmetry.

Key Claims Formalized #

  1. Proper names introduce C-PARAMS (referent binding) — ex. 28
  2. The running example has 5 C-PARAMS — ex. 32
  3. Parameter focussing yields clausal CE reading — ex. 53/54
  4. Parameter identification yields constituent CE reading — ex. 59/60
  5. Both coercions take the antecedent sign and target the same SAL-UTT
  6. Existential generalization removes a parameter without clarification — ex. 77/78
  7. Speaker resolves all params; addressee may not — ex. 82
  8. Partial assignment triggers PENDING, not grounding
  9. Updates require structured representations (Hybrid Content Hypothesis) — ex. 2/16

1994/2004 Clarification Ellipsis Apparatus #

This section was previously in Theories/Dialogue/KOS/Basic.lean §§6, 7, 8, 9, 10, 12, 15. It is paper-specific to @cite{ginzburg-cooper-2004}: in @cite{ginzburg-2012}, the corresponding machinery uses dgb-params (record types built on the shared CParam) rather than CtxtAssignment, and CCURs (Clarification Context Update Rules) rather than the three coercion operations (parameterFocussing, parameterIdentification, existentialGeneralization).

We preserve the 2004 formulation here because this study replicates the 2004 paper directly — the apparatus is single-consumer paper-specific content, demoted from substrate to consumer per the linglib pattern (cf. Core/FormFrequency.leanPhenomena/Case/Studies/Haspelmath2021.lean §0).

The shared substrate primitives — CParam, CParamSet, SubUtterance — remain in KOS/Defs.lean since they survive into the 2012 framework as the dgb-params/sub-constituents apparatus.

The four general theorems about coercion operations are namespaced under Apparatus to avoid colliding with this file's specific-instance theorems on the running example.

A contextual assignment maps parameter indices to values.

Grounding requires a total assignment (all C-PARAMS resolved). Clarification arises when the assignment is partial. @cite{ginzburg-cooper-2004} §6, ex. 81–82.

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        Does the assignment resolve a given parameter?

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          Does the assignment resolve all parameters in a set?

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            Which parameters remain unresolved?

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              An utterance skeleton: a sign with C-PARAMS and CONSTITS.

              The CONSTITS feature (ex. 30) provides access to all sub-utterances. C-PARAMS (ex. 28–29) are the contextual dependencies introduced by the sign, amalgamated from daughters via the Non-local Amalgamation Constraint. @cite{ginzburg-cooper-2004} §3.

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                  def GinzburgCooper2004.Apparatus.instDecidableEqUttSkeleton.decEq (x✝ x✝¹ : UttSkeleton) :
                  Decidable (x✝ = x✝¹)
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                    Find the constituent whose CONT matches a parameter index.

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                      A sign paired with a contextual assignment.

                      @cite{ginzburg-cooper-2004} ex. 81 p.353. The assignment f records which C-PARAMS have been resolved. Grounding checks whether f is total.

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                            Clarification Ellipsis processing state.

                            @cite{ginzburg-cooper-2004}: MAX-QUD and SAL-UTT are processing state for the CE analysis. These are NOT part of the @cite{ginzburg-2012} DGB or TIS (in 2012, MaxQUD is computed from the QUD poset's maximal element, not stored separately).

                            This state can be used alongside the 2012 TIS when CE processing is needed.

                            • maxQud : Option QContent

                              The currently maximal question — for CE coercion operations

                            • The salient sub-utterance — target of clarification

                            • pendingUtts : List SignAssignment

                              Pending utterances awaiting C-PARAMS resolution

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                              The three coercion operations on signs with unresolved C-PARAMS. @cite{ginzburg-cooper-2004} §5.

                              • paramFocussing : CoercionOp

                                Clausal CE reading: polar question about content (ex. 53)

                              • paramIdentification : CoercionOp

                                Constituent CE reading: wh-question about speaker meaning (ex. 59)

                              • existentialGeneralization : CoercionOp

                                Ground without clarification: ∃-quantify a parameter (ex. 77)

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                                  Output of a coercion operation: partial specification for the clarification context.

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                                        def GinzburgCooper2004.Apparatus.parameterFocussing (antecedent : UttSkeleton) (paramIdx : String) :

                                        Parameter focussing (@cite{ginzburg-cooper-2004} ex. 53): derive clausal CE reading.

                                        Takes the antecedent sign and a problematic parameter index. Finds the constituent that introduced the parameter. Produces MAX-QUD = polar question about the antecedent content.

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                                          Parameter identification (@cite{ginzburg-cooper-2004} ex. 59): derive constituent CE reading.

                                          Produces MAX-QUD = wh-question about speaker meaning.

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                                            Contextual existential generalization (@cite{ginzburg-cooper-2004} ex. 77): ground without clarifying.

                                            Removes a parameter from C-PARAMS by existentially quantifying it.

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                                              structure GinzburgCooper2004.Apparatus.IS (Fact QContent : Type) :

                                              Information State for the @cite{ginzburg-cooper-2004} model.

                                              Bundles a DGB with CE processing state (pending utterances). Uses String for both Fact and QContent, matching the string-based representations in the 2004 paper. The Participant type parameter is set to String, and the LocProp Cont is set to String since this is a 2004-era model.

                                              This is NOT the @cite{ginzburg-2012} TIS — it predates the genre/agenda private state. It exists to support the CE running example.

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                                                def GinzburgCooper2004.Apparatus.IS.initial {Fact QContent : Type} :
                                                IS Fact QContent

                                                An empty IS.

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                                                  def GinzburgCooper2004.Apparatus.IS.integrateUtterance {Fact QContent : Type} [BEq Fact] (is_ : IS Fact QContent) (skel : UttSkeleton) (assign : CtxtAssignment) (toFact : StringFact) :
                                                  IS Fact QContent

                                                  Integrate an utterance into the IS.

                                                  If the assignment fully resolves all C-PARAMS, the utterance is grounded: its content goes to FACTS. Otherwise, it goes to PENDING. @cite{ginzburg-cooper-2004} §6, ex. 82.

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                                                    def GinzburgCooper2004.Apparatus.IS.integrateUtteranceStr (is_ : IS String String) (skel : UttSkeleton) (assign : CtxtAssignment) :
                                                    IS String String

                                                    String-specialized integration (content IS the fact).

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                                                      def GinzburgCooper2004.Apparatus.IS.applyCoercion {Fact QContent : Type} (is_ : IS Fact QContent) (co : CoercionOutput) (toQ : StringQContent) :
                                                      IS Fact QContent

                                                      Apply a coercion output to the IS: set MAX-QUD and SAL-UTT.

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                                                        def GinzburgCooper2004.Apparatus.IS.applyCoercionStr (is_ : IS String String) (co : CoercionOutput) :
                                                        IS String String

                                                        String-specialized coercion application.

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                                                          Convert an UttSkeleton to a string-content LocProp. Subsumes the 2004 skeleton representation in the 2012 LocProp framework.

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                                                            Convert a LocProp String back to an UttSkeleton. Plain function (not LocProp.toSkeleton) because LocProp lives in Dialogue.KOS and dot notation looks there for the method, not in Apparatus. Use as locPropToSkeleton lp.

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                                                              Round-trip: UttSkeleton → LocProp → UttSkeleton is identity.

                                                              Both coercion operations target the same SAL-UTT.

                                                              theorem GinzburgCooper2004.Apparatus.coercions_different_op (ant : UttSkeleton) (idx : String) (r1 r2 : CoercionOutput) (h1 : parameterFocussing ant idx = some r1) (h2 : parameterIdentification ant idx = some r2) :
                                                              r1.op r2.op

                                                              The two coercion operations produce different operation types.

                                                              A fully resolved assignment leaves no unresolved parameters.

                                                              theorem GinzburgCooper2004.Apparatus.existential_gen_weakens (sk : UttSkeleton) (idx : String) :
                                                              List.length (existentialGeneralization sk idx).cparams List.length sk.cparams

                                                              Existential generalization never increases the parameter count.

                                                              C-PARAM for "Bo": binds variable b to the referent named "Bo". @cite{ginzburg-cooper-2004} ex. 28.

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                                                                C-PARAM for temporal precedence.

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                                                                  C-PARAM for speaker.

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                                                                    C-PARAM for addressee.

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                                                                      C-PARAM for utterance time.

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                                                                                Full utterance skeleton for "Did Bo leave?" with all 5 C-PARAMS. @cite{ginzburg-cooper-2004} ex. 32.

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                                                                                  Speaker (A) resolves all parameters: she knows who Bo is, who she is, who the addressee is, and the temporal parameters. @cite{ginzburg-cooper-2004} ex. 82b.

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                                                                                    Addressee (B) resolves all parameters EXCEPT b (Bo's referent). B doesn't know who "Bo" refers to. @cite{ginzburg-cooper-2004} ex. 82c.

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                                                                                      A's IS after uttering "Did Bo leave?": fully grounded. Speaker resolves all C-PARAMS, so the utterance goes straight to FACTS.

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                                                                                        B's IS after hearing "Did Bo leave?": partial assignment → pending. Addressee cannot resolve b, so the utterance goes to PENDING.

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                                                                                          Parameter focussing on "Bo" (parameter b): clausal CE reading. @cite{ginzburg-cooper-2004} ex. 53–54. Output: SAL-UTT = "Bo" constituent, MAX-QUD = ?b.ask(i,j,?.leave-rel(b,t)) Paraphrase: "Are you asking if b left?"

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                                                                                            Parameter identification on "Bo" (parameter b): constituent CE reading. @cite{ginzburg-cooper-2004} ex. 59–60. Output: SAL-UTT = "Bo" constituent, MAX-QUD = ?c.spkr-meaning-rel(addr,Bo,c) Paraphrase: "Who do you mean by Bo?"

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                                                                                              Existential generalization on "Bo" (parameter b). @cite{ginzburg-cooper-2004} ex. 77–78. Removes b from C-PARAMS, weakens content to ∃b.ask(i,j,?.leave-rel(b,t)).

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                                                                                                B applies parameter focussing to set up clarification context.

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                                                                                                  B applies parameter identification to set up clarification context.

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                                                                                                    The running example has exactly 5 C-PARAMS.

                                                                                                    The running example has 4 constituents (Did, Bo, leave, Did Bo leave).

                                                                                                    Addressee does NOT resolve all C-PARAMS (missing b).

                                                                                                    theorem GinzburgCooper2004.speaker_grounds :
                                                                                                    speakerIS.dgb.facts = ["ask(i,j,?.leave-rel(b,t))"]

                                                                                                    Speaker's utterance is grounded (added to FACTS).

                                                                                                    Addressee's utterance is NOT grounded (no new facts).

                                                                                                    Addressee's utterance goes to PENDING.

                                                                                                    Parameter focussing succeeds on "Bo".

                                                                                                    Parameter identification succeeds on "Bo".

                                                                                                    Both coercions target the same SAL-UTT (the "Bo" constituent).

                                                                                                    theorem GinzburgCooper2004.salUtt_is_bo :
                                                                                                    Option.map (fun (x : Apparatus.CoercionOutput) => x.salUtt.phon) focussingOnBo = some "Bo"

                                                                                                    The SAL-UTT is "Bo".

                                                                                                    Focussing and identification produce different operation types.

                                                                                                    Existential generalization removes exactly one parameter.

                                                                                                    The removed parameter is b.

                                                                                                    theorem GinzburgCooper2004.exist_gen_weakens_content :
                                                                                                    existGenOnBo.cont = "∃b.ask(i,j,?.leave-rel(b,t))"

                                                                                                    Existential generalization wraps content with ∃.

                                                                                                    theorem GinzburgCooper2004.focussing_maxqud :
                                                                                                    Option.map (fun (x : Apparatus.CoercionOutput) => x.maxQud) focussingOnBo = some "?b.ask(i,j,?.leave-rel(b,t))"

                                                                                                    Focussing MAX-QUD is a question about the antecedent content.

                                                                                                    theorem GinzburgCooper2004.identification_maxqud :
                                                                                                    Option.map (fun (x : Apparatus.CoercionOutput) => x.maxQud) identificationOnBo = some "?c.spkr-meaning-rel(addr,Bo,c)"

                                                                                                    Identification MAX-QUD is a speaker-meaning question.

                                                                                                    Hybrid Content Hypothesis (@cite{ginzburg-cooper-2004} ex. 2/16): The content updated in dynamic semantics consists of structure expressing detailed relationships between the content and formal properties (syntax, phonology etc) of the various parts of an utterance.

                                                                                                    Evidence: The same propositional content ("Bo left") yields different clarification potentials depending on phonological/syntactic structure. The utterance skeleton encodes this structure via CONSTITS and C-PARAMS.