2016/01/25 by Dong Nguyen, Jacob Eisenstein, Nguyen, Dong +1
Computer Science · Social Sciences · #Authorship Attribution and Profiling #Computation and Language (cs.CL) #Computational and Text Analysis Methods #FOS: Computer and information sciences #Linguistic Variation and Morphology #cs.CL
paper · pdf · doi:10.48550/arxiv.1601.06579
In submission. 26 pages
openalex publication_date 2016/01/25 · arxiv created 2016/08/29 · arxiv updated 2016/08/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Quantifying the degree of spatial dependence for linguistic variables is a key task for analyzing dialectal variation. However, existing approaches have important drawbacks. First, they are based on parametric models of dependence, which limits their power in cases where the underlying parametric assumptions are violated. Second, they are not applicable to all types of linguistic data: some approaches apply only to frequencies, others to boolean indicators of whether a linguistic variable is present. We present a new method for measuring geographical language variation, which solves both of these problems. Our approach builds on Reproducing Kernel Hilbert space (RKHS) representations for nonparametric statistics, and takes the form of a test statistic that is computed from pairs of individual geotagged observations without aggregation into predefined geographical bins. We compare this test with prior work using synthetic data as well as a diverse set of real datasets: a corpus of Dutch tweets, a Dutch syntactic atlas, and a dataset of letters to the editor in North American newspapers. Our proposed test is shown to support robust inferences across a broad range of scenarios and types of data.