# Parsing with CYK over Distributed Representations

arXiv: https://arxiv.org/abs/1705.08843 · HTML: https://mlsanity.com/paper/1705.08843/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2017.

## Built on by (most cited first, 2 of 2)

- [ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits](https://mlsanity.com/paper/2604.06559/) (2026)
- [Neuro-symbolic Syntactic Parsing: Shaping a Neural Network with the CYK Algorithm](https://mlsanity.com/paper/2605.31421/) (2026)

## Abstract (arXiv)

Syntactic parsing is a key task in natural language processing. This task has been dominated by symbolic, grammar-based parsers. Neural networks, with their distributed representations, are challenging these methods. In this article we show that existing symbolic parsing algorithms can cross the border and be entirely formulated over distributed representations. To this end we introduce a version of the traditional Cocke-Younger-Kasami (CYK) algorithm, called D-CYK, which is entirely defined over distributed representations. Our D-CYK uses matrix multiplication on real number matrices of size independent of the length of the input string. These operations are compatible with traditional neural networks. Experiments show that our D-CYK approximates the original CYK algorithm. By showing that CYK can be entirely performed on distributed representations, we open the way to the definition of recurrent layers of CYK-informed neural networks.