{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-kernel-independence-test-for-geographical","title":"A Kernel Independence Test for Geographical Language Variation","arxiv_id":"1601.06579","date":"2016-01-25","proceeding":"CL 2017 9","authors":["Dong Nguyen","Jacob Eisenstein"],"abstract":"Quantifying the degree of spatial dependence for linguistic variables is a\nkey task for analyzing dialectal variation. However, existing approaches have\nimportant drawbacks. First, they are based on parametric models of dependence,\nwhich limits their power in cases where the underlying parametric assumptions\nare violated. Second, they are not applicable to all types of linguistic data:\nsome approaches apply only to frequencies, others to boolean indicators of\nwhether a linguistic variable is present. We present a new method for measuring\ngeographical language variation, which solves both of these problems. Our\napproach builds on Reproducing Kernel Hilbert space (RKHS) representations for\nnonparametric statistics, and takes the form of a test statistic that is\ncomputed from pairs of individual geotagged observations without aggregation\ninto predefined geographical bins. We compare this test with prior work using\nsynthetic data as well as a diverse set of real datasets: a corpus of Dutch\ntweets, a Dutch syntactic atlas, and a dataset of letters to the editor in\nNorth American newspapers. Our proposed test is shown to support robust\ninferences across a broad range of scenarios and types of data.","url_abs":"http://arxiv.org/abs/1601.06579v2","url_pdf":"http://arxiv.org/pdf/1601.06579v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-kernel-independence-test-for-geographical","repo_url":"https://github.com/dongpng/geo-independence-testing","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}