{"title":"Modeling Variation in Taiwan Southern Min Syllable Contraction","authors":"Yingshing Li, J. Myers","doi":"10.6519/TJL.2005.3(2).3","DOIUrl":null,"url":null,"abstract":"In this paper we attempt to model variation in Taiwan Southern Min syllable contraction using the Gradual Learning Algorithm (GLA; Boersma and Hayes 2001), an Optimality-Theoretic model with variable constraint ranking. To explore the effectiveness of GLA, we look at three data sets of increasing complexity: non-variable fully contracted forms as analyzed by Hsu (2003), variable outputs as noted by Hsu and confirmed by other native speakers, and phonetically variable outputs collected in a speech production experiment by Li (2005). The results reveal that GLA is capable of providing plausible constraint ranking hierarchies that capture both major generalizations and variability. Stochastic constraint evaluation thus seems to be a promising mechanism in the construction of grammars.","PeriodicalId":41000,"journal":{"name":"Taiwan Journal of Linguistics","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Taiwan Journal of Linguistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.6519/TJL.2005.3(2).3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper we attempt to model variation in Taiwan Southern Min syllable contraction using the Gradual Learning Algorithm (GLA; Boersma and Hayes 2001), an Optimality-Theoretic model with variable constraint ranking. To explore the effectiveness of GLA, we look at three data sets of increasing complexity: non-variable fully contracted forms as analyzed by Hsu (2003), variable outputs as noted by Hsu and confirmed by other native speakers, and phonetically variable outputs collected in a speech production experiment by Li (2005). The results reveal that GLA is capable of providing plausible constraint ranking hierarchies that capture both major generalizations and variability. Stochastic constraint evaluation thus seems to be a promising mechanism in the construction of grammars.
本文尝试使用渐进式学习演算法(GLA;Boersma and Hayes 2001)，一个具有可变约束排序的最优性理论模型。为了探索GLA的有效性，我们研究了三个日益复杂的数据集:Hsu(2003)分析的非变量完全收缩形式，Hsu注意到并得到其他母语人士证实的变量输出，以及Li(2005)在语音生成实验中收集的语音变量输出。结果表明，GLA能够提供合理的约束排序层次结构，同时捕获主要的泛化和可变性。因此，随机约束评价似乎是一种很有前途的语法构建机制。
期刊介绍：
Taiwan Journal of Linguistics is an international journal dedicated to the publication of research papers in linguistics and welcomes contributions in all areas of the scientific study of language. Contributions may be submitted from all countries and are accepted all year round. The language of publication is English. There are no restrictions on regular submission; however, manuscripts simultaneously submitted to other publications cannot be accepted. TJL adheres to a strict standard of double-blind reviews to minimize biases that might be caused by knowledge of the author’s gender, culture, or standing within the professional community. Once a manuscript is determined as potentially suitable for the journal after an initial screening by the editor, all information that may identify the author is removed, and copies are sent to at least two qualified reviewers. The selection of reviewers is based purely on professional considerations and their identity will be kept strictly confidential by TJL. All feedback from the reviewers, except such comments as may be specifically referred to the attention of the editor, is faithfully relayed to the authors to assist them in improving their work, regardless of whether the paper is to be accepted, accepted upon minor revision, revised and resubmitted, or rejected.
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