Heat Kernel Analysis of Syntactic Structures
Name
11786_2021_498_ReferencePDF.pdf
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7.25 MB
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277fcbd317910f8c9c0baa4c3f2a04ff
Author(s)
Ortegaray, Andrew
Berwick, Robert C.
Marcolli, Matilde
Date Issued
February 2021
Journal
Mathematics in Computer Science
Publisher
Springer International Publishing
Citation
Ortegaray, A., Berwick, R.C. & Marcolli, M. Heat Kernel Analysis of Syntactic Structures. Math.Comput.Sci. 15, 643–660 (2021)
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11786-021-00498-0
