Mapping from CCG categories to abstract symbols.
For the cross-serial pattern:
- NP maps to 'a' or 'b' (arguments)
- V maps to 'c' or 'd' (verbs)
Equations
- One or more equations did not get rendered due to their size.
Instances For
A CCG derivation annotated with surface words.
Used here for the generative capacity proof; the full
AnnotatedDerivation with binding information is in the
bridge file Phenomena.FillerGap.CCG_CrossSerialBridge.
- deriv : CrossSerial.ExtDerivStep
The derivation
Surface words
Instances For
Equations
- One or more equations did not get rendered due to their size.
Instances For
Simple annotated derivation for "Jan Piet zag zwemmen".
Equations
- CCG.GenerativeCapacity.dutch_2v_deriv = { deriv := CCG.CrossSerial.jan_zag_zwemmen_piet, words := ["Jan", "Piet", "zag", "zwemmen"] }
Instances For
A CCG derivation for 2-fold cross-serial dependencies yields a string of length 2 * 2 = 4 (the "Jan Piet zag zwemmen" pattern).
The derivation structure (via B² composition) ensures that:
- 2 NPs yield the argument positions
- 2 Vs yield the verb positions
- The composition threads arguments through verbs correctly
The language of CCG derivations for cross-serial dependencies includes {aⁿbⁿcⁿdⁿ}.
More precisely: for each n, there exists a CCG derivation whose yield corresponds to aⁿbⁿcⁿdⁿ.
CCG is strictly more expressive than CFG.
- CCG generates {aⁿbⁿcⁿdⁿ} (via generalized composition)
- {aⁿbⁿcⁿdⁿ} lacks the CFL pumping property (hence is not context-free)
- Therefore: CCG can generate languages that CFG cannot
CCG strictly exceeds context-free power: it generates {aⁿbⁿcⁿdⁿ}, which is not context-free.