{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:24:36Z","timestamp":1766269476323},"reference-count":4,"publisher":"MIT Press - Journals","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["TACL"],"published-print":{"date-parts":[[2013,12]]},"abstract":"<jats:p> This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks. <\/jats:p>","DOI":"10.1162\/tacl_a_00225","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T15:42:12Z","timestamp":1546011732000},"page":"255-266","source":"Crossref","is-referenced-by-count":12,"title":["Minimally-Supervised Morphological Segmentation using Adaptor                     Grammars"],"prefix":"10.1162","volume":"1","author":[{"given":"Kairit","family":"Sirts","sequence":"first","affiliation":[{"name":"Institute of Cybernetics, Tallinn University of Technology,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharon","family":"Goldwater","sequence":"additional","affiliation":[{"name":"School of Informatics, The University of Edinburgh,"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"issue":"1","key":"p_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1187415.1187418","volume":"4","author":"Creutz Mathias","year":"2007","journal-title":"ACM Transactions of Speech and Language Processing"},{"key":"p_3","doi-asserted-by":"publisher","DOI":"10.1162\/089120101750300490"},{"key":"p_10","first-page":"641","author":"Johnson Mark","year":"2007","journal-title":"Advances in Neural Information Processing Systems 19, pages"},{"issue":"2","key":"p_18","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1214\/aop\/1024404422","volume":"25","author":"Pitman Jim","year":"1997","journal-title":"Annals of Probability"}],"container-title":["Transactions of the Association for Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/tacl_a_00225","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T21:39:10Z","timestamp":1615585150000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/tacl\/article\/43209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,12]]},"references-count":4,"alternative-id":["10.1162\/tacl_a_00225"],"URL":"https:\/\/doi.org\/10.1162\/tacl_a_00225","relation":{},"ISSN":["2307-387X"],"issn-type":[{"value":"2307-387X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,12]]}}}