Documentation

Linglib.Studies.Steedman2000

Steedman 2000: The Syntactic Process #

CCG predictions from [Ste00a], one section per phenomenon:

Word order #

Slash direction encodes word order: TV = (S\NP)/NP looks right for the object NP first, then the resulting S\NP looks left for the subject, enforcing SVO.

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        Non-constituent coordination #

        CCG derives "John likes and Mary hates beans" (modeled on the book's "Anna married, and I detest, Manny") with category S: "John likes" is a constituent S/NP via type-raising + composition.

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          Non-constituent coordination requires more combinatory operations than standard coordination. Reading operation count as processing difficulty is this formalization's linking hypothesis, not a claim of [Ste00a].

          Toy semantic lexicon over the toy English fragment ("likes"/"hates" reuse sees_sem as placeholder denotations).

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            "John sees Mary" with a type-raised subject: the raised subject CCG.john_tr : S/(S\NP) uses forward application, and the derivation produces the same truth value as the canonical one.

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              The predicate "John likes and Mary hates" (category S/NP) evaluated at an entity.

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                The pointwise conjunction of "John likes" and "Mary hates" at an entity.

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                  Generalized conjunction delivers the conjunctive interpretation: ⟦John likes and Mary hates⟧(e) = ⟦John likes⟧(e) ∧ ⟦Mary hates⟧(e).

                  The spelled-out paraphrase "John likes beans and Mary hates beans".

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                    The non-constituent coordination and its spelled-out paraphrase receive the same truth conditions — the book's claim that the composed derivation yields the same predicate-argument structure as the canonical one.

                    The coordinator's role is truth-conditionally load-bearing #

                    interp reads the coordinator's role off the .coord node — it no longer hardcodes conjunction — so which coordinator a derivation uses is part of its truth conditions. Using the actual English fragment coordinators, conjoining a true sentence p and a false sentence q with and_ (role = .j) gives p ∧ q (false), while or_ (role = .disj) gives p ∨ q (true). They differ, so the marking's role field is load-bearing — flipping English.Coordination.and_.role to .disj would break the theorem below, rather than no theorem depending on it.

                    The coordinator's role flips the truth conditions: English and_ yields p ∧ q, or_ yields p ∨ q, and these differ at p = ⊤, q = ⊥. Flipping a fragment coordinator's role collapses the inequality, so the role marking is not decorative.

                    Gapping #

                    [Ros70]'s generalization — gapping direction tracks word order — which [Ste00a] derives from the Principles of Adjacency, Consistency, and Inheritance together with the order-preserving constraint on type-raising. Deriving predictedGappingPattern from the slash directions of CCG.Gapping's gapped categories is TODO. (Dutch licensing both directions is mixed_allows_both.)

                    Basic word order of a transitive clause (S = subject, V = verb, O = object).

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                        Direction of gapping in a coordinate structure: forward gapping leaves the gap in the non-initial conjunct ("Dexter ate bread, and Warren, potatoes"); backward gapping leaves it in the non-final conjunct (Japanese "Ken-ga Naomi-o, Erika-ga Sara-o tazuneta").

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                            The gapping directions a language allows.

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                                      The gapping directions CCG predicts for a word order: forward gapping needs a leftward-looking gapped conjunct, available through backward type-raising over rightward-seeking verbs (T\(T/NP)); backward gapping needs forward raising over leftward-seeking verbs (T/(T\NP)).

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                                        The CCG-predicted pattern coincides with Ross's generalization.

                                        English (SVO) has no leftward-looking transitive verb category, so the rightward-looking gapped conjunct a backward gap needs cannot be built: "*Warren, potatoes and Dexter ate bread" (instantiating Steedman's *SO and SVO schema; the book's attested forward counterpart is "Dexter ate bread and Warren, potatoes").

                                        Main- vs subordinate-clause word order, for languages whose two clause types diverge.

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                                            Steedman's revision of [Ros70]: gapping availability tracks the lexical availability of verb categories, not a single "underlying" word order — forward gapping needs rightward-combining verbs, backward gapping leftward-combining verbs in either clause type.

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                                              Dutch: SVO main clauses, SOV subordinate clauses. The mixed profile licenses both gapping directions — forward in main clauses ("Wil jij een ijsje en Marietje limonade?"), backward in subordinate clauses ("...dat Jan Syntactic Structures en Piet Aspects gelezen heeft").

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                                                A mixed-order language like Dutch licenses both gapping directions.

                                                Steedman's taxonomy of elliptical constructions.

                                                • gapping : EllipsisType

                                                  "Dexter ate bread, and Warren, potatoes"

                                                • stripping : EllipsisType

                                                  "Dexter ran away, and Warren (too)"

                                                • vpEllipsis : EllipsisType

                                                  "Dexter ate bread, and Warren did too"

                                                • sluicing : EllipsisType

                                                  "Dexter did something, but I don't know what"

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                                                    All four of Steedman's elliptical constructions are surface anaphora in Hankamer & Sag's sense ([HS76]): each deletes internal structure under identity with a linguistic antecedent. Steedman's taxonomy contains no deep anaphor (no do so-type pro-form), so the depth axis is constant .surface over it.

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                                                    Cross-framework non-alignment. Steedman's CCG cut isSyntacticallyMediated (gapping/stripping derived by category composition; VP-ellipsis/sluicing handled anaphorically) is not Hankamer & Sag's deep/surface cut. VP-ellipsis is the paradigm surface anaphor ([HS76]; Landau's own surface baseline in [Lan26]) yet CCG treats it as non-mediated — so the two frameworks partition the very same constructions differently.

                                                    Cross-serial dependencies #

                                                    Dutch verb clusters ([BKPZ82]) with cross-serial NP-verb bindings, using the surface-faithful derivations of CCG.CrossSerial: cluster verbs carry leftward NP slots and the cluster composes by forward crossed composition, per the book's own Dutch fragment, so the yield matches the attested word order.

                                                    A CCG derivation annotated with which NP binds to which verb. TODO: compute binding from deriv's composition structure instead of annotating it by hand.

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                                                        "Jan Piet zag zwemmen" with cross-serial bindings: Jan is the subject of "zag", Piet the argument bound into the cluster.

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                                                          "Jan Piet Marie zag helpen zwemmen": zag >B× (helpen zwemmen) makes the cluster a leftward-seeking 3-place predicate; Marie binds helpen's slot, Piet zag's object slot, Jan the subject — the cross-serial binding pattern, in the attested word order.

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                                                            Verb clusters and quantifier scope (§6.8) #

                                                            Scope tracks word order: in the verb-raising order the cluster forms by composition, so a quantified argument combines with a function containing the tensed verb and can take scope over it; in the verb-projection-raising order it combines with the embedded verb alone. The derivations below are category-checked DerivStep trees: the verb-raising cluster forms by forward crossed composition (CCG.forwardCompX), the verb-projection-raising order by plain application — the composed-cluster vs. applied-cluster contrast driving the account. (The toy Cat still drops the book's features, e.g. the VP₋SUB restriction on >B×.)

                                                            Word order in a West Germanic verb cluster ([Ste00a] §6.8).

                                                            • verbRaising : VerbOrder

                                                              Object precedes the whole verb cluster: NP … V_emb V_matrix.

                                                            • verbProjectionRaising : VerbOrder

                                                              Object follows the matrix verb: V_matrix … NP V_emb.

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                                                                Verb-raising order, Dutch (99a): the cluster probeert te zingen forms by crossed composition before taking the object to its left.

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                                                                  Verb-projection-raising order, Dutch (99b): the matrix verb applies to an already-saturated embedded VP, so the quantified object never combines with a function containing the tensed verb.

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                                                                    Both derivations are category-valid and derive the same category.

                                                                    Scope availability as CCG predicts it: analyze the derivation the word order forces and read availability off its derivation type.

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                                                                      Read the §6.8 word-order classification off an example's paperFeatures.

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                                                                        Observed scope availability: the judgment on the example's "inverse" reading (the "surface" reading is acceptable throughout §6.8).

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                                                                          The §6.8 data as (word order, observed availability) pairs.

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                                                                            The CCG prediction matches every §6.8 judgment.

                                                                            Truth-conditional pipeline #

                                                                            The complete CCG → Montague pipeline over the toy fragment: derivations interpreted compositionally, each checked against the toy model.

                                                                            "John sleeps" - backward application

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                                                                              "Mary sleeps" - backward application

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                                                                                "John laughs" - backward application

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                                                                                  "Mary laughs" - backward application

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                                                                                    "John sees Mary" - forward then backward application

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                                                                                      "Mary sees John" - forward then backward application

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                                                                                        "John eats pizza" - forward then backward application

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                                                                                          Get meaning (as Prop) from CCG derivation

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                                                                                            CCG correctly predicts "John sleeps" is true

                                                                                            CCG correctly predicts "Mary sleeps" is false

                                                                                            CCG correctly predicts "John laughs" is true

                                                                                            CCG correctly predicts "Mary laughs" is true

                                                                                            CCG correctly predicts "John sees Mary" is true

                                                                                            CCG correctly predicts "Mary sees John" is true