mlsanityData to 7 Oct 2026

A CCG-Based Version of the DisCoCat Framework

arXiv 2105.07720 ↗First posted 17 May 2021cs.CL math.CTcited by 16

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Abstract (arXiv)

While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for studying compositional aspects of language at the level of semantics, its strong dependency on pregroup grammars poses important restrictions: first, it prevents large-scale experimentation due to the absence of a pregroup parser; and second, it limits the expressibility of the model to context-free grammars. In this paper we solve these problems by reformulating DisCoCat as a passage from Combinatory Categorial Grammar (CCG) to a category of semantics. We start by showing that standard categorial grammars can be expressed as a biclosed category, where all rules emerge as currying/uncurrying the identity; we then proceed to model permutation-inducing rules by exploiting the symmetry of the compact closed category encoding the word meaning. We provide a proof of concept for our method, converting "Alice in Wonderland" into DisCoCat form, a corpus that we make available to the community.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 7 of its 31 references that are in the map. First posted on arXiv 17 May 2021.

Builds on

7 of 31 references.

References · 7

  1. 2021QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computercited by 65
  2. 2017A* CCG Parsing with a Supertag and Dependency Factored Modelcited by 22
  3. 2015Open System Categorical Quantum Semantics in Natural Language Processingcited by 17
  4. 2013Lambek vs. Lambek: Functorial Vector Space Semantics and String Diagrams for Lambek Calculuscited by 13
  5. 2014Reasoning about Meaning in Natural Language with Compact Closed Categories and Frobenius Algebrascited by 7
  6. 2016Graded Entailment for Compositional Distributional Semanticscited by 3
  7. 2013Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semanticscited by 1

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Citing papers per year: 2021: 5; 2022: 2; 2023: 7; 2024: 1; 2025: 1.

16 papers.

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