{"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/the-global-anchor-method-for-quantifying","title":"The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation","arxiv_id":"1812.10382","date":"2018-12-12","proceeding":"NeurIPS 2018 12","authors":["Zi Yin","Vin Sachidananda","Balaji Prabhakar"],"abstract":"Language is dynamic, constantly evolving and adapting with respect to time,\ndomain or topic. The adaptability of language is an active research area, where\nresearchers discover social, cultural and domain-specific changes in language\nusing distributional tools such as word embeddings. In this paper, we introduce\nthe global anchor method for detecting corpus-level language shifts. We show\nboth theoretically and empirically that the global anchor method is equivalent\nto the alignment method, a widely-used method for comparing word embeddings, in\nterms of detecting corpus-level language shifts. Despite their equivalence in\nterms of detection abilities, we demonstrate that the global anchor method is\nsuperior in terms of applicability as it can compare embeddings of different\ndimensionalities. Furthermore, the global anchor method has implementation and\nparallelization advantages. We show that the global anchor method reveals fine\nstructures in the evolution of language and domain adaptation. When combined\nwith the graph Laplacian technique, the global anchor method recovers the\nevolution trajectory and domain clustering of disparate text corpora.","url_abs":"http://arxiv.org/abs/1812.10382v1","url_pdf":"http://arxiv.org/pdf/1812.10382v1.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":"the-global-anchor-method-for-quantifying","repo_url":"https://github.com/aaaasssddf/global-anchor-method","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"the-global-anchor-method-for-quantifying","repo_url":"https://github.com/ziyin-dl/global-anchor-method","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.10382","atlas_url":"https://app.syntology.ai/?focus=1812.10382","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}