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In English, for example, words of Germanic and Latinate origin exhibit different stress patterns, and a certain syntactic structure\u2014double-object datives\u2014is predominantly associated with Germanic verbs rather than Latinate verbs. From the perspective of language acquisition, however, such etymology-based generalizations raise learnability concerns, since the historical origins of words are presumably inaccessible information for general language learners. In this study, we present computational evidence indicating that the Germanic-Latinate distinction in the English lexicon is learnable from the phonotactic information of individual words. Specifically, we performed an unsupervised clustering on corpus-extracted words, and the resulting word clusters largely aligned with the etymological distinction. The model-discovered clusters also recovered various linguistic generalizations documented in the previous literature regarding the corresponding etymological classes. Moreover, our model also uncovered previously unrecognized features of the quasi-etymological clusters. Taken together with prior results from Japanese, our findings indicate that the proposed method provides a general, cross-linguistic approach to discovering etymological structure from phonotactic cues in the lexicon.<\/jats:p>","DOI":"10.1162\/opmi.a.346","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:01:57Z","timestamp":1776369717000},"page":"478-501","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["Unsupervised Classification of English Words Based on Phonological Information: Discovery of Germanic and Latinate Clusters"],"prefix":"10.1162","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3900-5410","authenticated-orcid":true,"given":"Takashi","family":"Morita","sequence":"first","affiliation":[{"name":"Academy of Emerging Sciences, Chubu University, Kasugai, Japan"},{"name":"Institute for Advanced Research, Nagoya University, Nagoya, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timothy J.","family":"O\u2019Donnell","sequence":"additional","affiliation":[{"name":"Department of Linguistics, McGill University, Montr\u00e9al, Canada"},{"name":"Canada CIFAR AI Chair, Mila, Montr\u00e9al, Canada"},{"name":"Mila\u2014Quebec AI Institute, Montr\u00e9al, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"2026042111304900200_bib1","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771730","volume-title":"The adaptive character of thought","author":"Anderson","year":"1990"},{"key":"2026042111304900200_bib2","volume-title":"The role of etymology and word-length in English word formation","author":"Anshen","year":"1986"},{"issue":"6","key":"2026042111304900200_bib3","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1214\/aos\/1176342871","article-title":"Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems","volume":"2","author":"Antoniak","year":"1974","journal-title":"Annals of Statistics"},{"key":"2026042111304900200_bib4","volume-title":"The CELEX lexical database (Release 2)","author":"Baayen","year":"1995"},{"issue":"3","key":"2026042111304900200_bib5","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1162\/LING_a_00217","article-title":"Source-oriented generalizations as grammar inference in Russian vowel deletion","volume":"47","author":"Becker","year":"2016","journal-title":"Linguistic Inquiry"},{"key":"2026042111304900200_bib6","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316870","volume-title":"Bayesian theory","author":"Bernardo","year":"1994"},{"key":"2026042111304900200_bib7","volume-title":"Pattern recognition and machine learning","author":"Bishop","year":"2006"},{"issue":"1","key":"2026042111304900200_bib8","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1214\/06-BA104","article-title":"Variational inference for Dirichlet process mixtures","volume":"1","author":"Blei","year":"2006","journal-title":"Bayesian Analysis"},{"issue":"518","key":"2026042111304900200_bib9","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","article-title":"Variational inference: A review for statisticians","volume":"112","author":"Blei","year":"2017","journal-title":"Journal of the American Statistical Association"},{"key":"2026042111304900200_bib10","doi-asserted-by":"publisher","first-page":"14","DOI":"10.7282\/T36M34WM","article-title":"Mostly predictable: Cyclicity and the distribution of schwa in Itelmen","volume-title":"Proceedings of the Twenty-Sixth Western Conference on Linguistics (WECOL)","author":"Bobaljik","year":"1997"},{"key":"2026042111304900200_bib11","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1515\/9783110198621.75","article-title":"Is syntactic knowledge probabilistic? 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