Heat Kernel Analysis of Syntactic Structures
Copyright policy )Heat Kernel Analysis of Syntactic Structures
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.
20 pages, LaTeX, png figures
- Massachusetts Institute of Technology United States
- University of Toronto Canada
- California Institute of Technology United States
- Perimeter Institute Canada
FOS: Computer and information sciences, Statistical aspects of big data and data science, Computer Science - Computation and Language, Classification and discrimination; cluster analysis (statistical aspects), heat kernel, generative linguistics, dimensional reduction, Linguistics, clustering coefficients, Computation and Language (cs.CL), Heat kernel, syntactic parameters
FOS: Computer and information sciences, Statistical aspects of big data and data science, Computer Science - Computation and Language, Classification and discrimination; cluster analysis (statistical aspects), heat kernel, generative linguistics, dimensional reduction, Linguistics, clustering coefficients, Computation and Language (cs.CL), Heat kernel, syntactic parameters
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