{"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/monitoring-term-drift-based-on-semantic","title":"Monitoring Term Drift Based on Semantic Consistency in an Evolving Vector Field","arxiv_id":"1502.01753","date":"2015-02-05","proceeding":null,"authors":["Peter Wittek","Sándor Darányi","Efstratios Kontopoulos","Theodoros Moysiadis","Ioannis Kompatsiaris"],"abstract":"Based on the Aristotelian concept of potentiality vs. actuality allowing for\nthe study of energy and dynamics in language, we propose a field approach to\nlexical analysis. Falling back on the distributional hypothesis to\nstatistically model word meaning, we used evolving fields as a metaphor to\nexpress time-dependent changes in a vector space model by a combination of\nrandom indexing and evolving self-organizing maps (ESOM). To monitor semantic\ndrifts within the observation period, an experiment was carried out on the term\nspace of a collection of 12.8 million Amazon book reviews. For evaluation, the\nsemantic consistency of ESOM term clusters was compared with their respective\nneighbourhoods in WordNet, and contrasted with distances among term vectors by\nrandom indexing. We found that at 0.05 level of significance, the terms in the\nclusters showed a high level of semantic consistency. Tracking the drift of\ndistributional patterns in the term space across time periods, we found that\nconsistency decreased, but not at a statistically significant level. Our method\nis highly scalable, with interpretations in philosophy.","url_abs":"http://arxiv.org/abs/1502.01753v1","url_pdf":"http://arxiv.org/pdf/1502.01753v1.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":"monitoring-term-drift-based-on-semantic","repo_url":"https://github.com/peterwittek/concept_drifts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"lexical-analysis","task_name":"Lexical Analysis"},{"task_slug":"philosophy","task_name":"Philosophy"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}