# Towards Compositional Distributional Discourse Analysis

arXiv: https://arxiv.org/abs/1811.03277 · HTML: https://mlsanity.com/paper/1811.03277/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [On Meta-Prompting](https://mlsanity.com/paper/2312.06562/) (2023)
- [How to make qubits speak](https://mlsanity.com/paper/2107.06776/) (2021)
- [Functorial Language Models](https://mlsanity.com/paper/2103.14411/) (2021)
- [Pregroup Grammars, their Syntax and Semantics](https://mlsanity.com/paper/2109.11237/) (2021)

## Abstract (arXiv)

Categorical compositional distributional semantics provide a method to derive the meaning of a sentence from the meaning of its individual words: the grammatical reduction of a sentence automatically induces a linear map for composing the word vectors obtained from distributional semantics. In this paper, we extend this passage from word-to-sentence to sentence-to-discourse composition. To achieve this we introduce a notion of basic anaphoric discourses as a mid-level representation between natural language discourse formalised in terms of basic discourse representation structures (DRS); and knowledge base queries over the Semantic Web as described by basic graph patterns in the Resource Description Framework (RDF). This provides a high-level specification for compositional algorithms for question answering and anaphora resolution, and allows us to give a picture of natural language understanding as a process involving both statistical and logical resources.