Categorical Vector Space Semantics for Lambek Calculus with a Relevant Modality
Lachlan McPheat, Mehrnoosh Sadrzadeh, Hadi Wazni, Gijs Wijnholds
TL;DR
This work develops a categorical–vector-space semantics for the Lambek Calculus with a relevant modality $!L^*$ to model controlled contraction and permutation, enabling DisCoCat-style compositional semantics of parasitic-gap phenomena. It defines a $ extbf{!L^*}$-category $oldsymbol{ ext{C}( ext{!L}^*)}$ with a coalgebra modality, and instantiates it in $ extbf{FdVect}$ via a quantisation functor, offering three concrete $!$-functors. The authors introduce diagrammatic clasp tooling and provide linguistic derivations for parasitic-gap examples, complemented by an experimental validation using extended disambiguation data and multiple neural embeddings; Full copying and Cofree-inspired copying perform best among linear models, with BERT providing the strongest overall baseline. The results suggest that the proposed categorical, vector-space approach can rival traditional additive embeddings in handling grammatically guided copying phenomena and motivates further work on coherence, differential-category connections, and bounded modalities for scalable parsing.
Abstract
We develop a categorical compositional distributional semantics for Lambek Calculus with a Relevant Modality !L*, which has a limited edition of the contraction and permutation rules. The categorical part of the semantics is a monoidal biclosed category with a coalgebra modality, very similar to the structure of a Differential Category. We instantiate this category to finite dimensional vector spaces and linear maps via "quantisation" functors and work with three concrete interpretations of the coalgebra modality. We apply the model to construct categorical and concrete semantic interpretations for the motivating example of !L*: the derivation of a phrase with a parasitic gap. The effectiveness of the concrete interpretations are evaluated via a disambiguation task, on an extension of a sentence disambiguation dataset to parasitic gap phrases, using BERT, Word2Vec, and FastText vectors and Relational tensors.
