# Heat Kernel analysis of Syntactic Structures

arXiv: https://arxiv.org/abs/1803.09832 · HTML: https://mlsanity.com/paper/1803.09832/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2018.

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

- [Mathematical Structure of Syntactic Merge](https://mlsanity.com/paper/2305.18278/) (2023)
- [Syntactic structures and the general Markov models](https://mlsanity.com/paper/2104.08462/) (2021)

## Abstract (arXiv)

We consider two different data sets of syntactic parameters and we discuss how to detect relations between parameters through a heat kernel method developed by Belkin-Niyogi, which produces low dimensional representations of the data, based on Laplace eigenfunctions, that preserve neighborhood information. We analyze the different connectivity and clustering structures that arise in the two datasets, and the regions of maximal variance in the two-parameter space of the Belkin-Niyogi construction, which identify preferable choices of independent variables. We compute clustering coefficients and their variance.