{"title":"波斯语衍生词尾生产力:一个模糊分析","authors":"S. Z. Aftabi, A. Ahangar, H. M. Nehi","doi":"10.1080/09296174.2021.1887575","DOIUrl":null,"url":null,"abstract":"ABSTRACT The main aim of this article is to introduce a new way of dealing with the vague concept of suffix productivity in Persian. This approach, that is fuzzy set theory, gives each suffix a degree of membership from [0,1] to different productivity categories. To estimate morphological productivity of Persian suffixes, first Baayen’s proposed measures, i.e. realized productivity, expanding productivity and potential productivity were applied to Bijankhan corpus. Correspondingly, 2.6 million words in the corpus were investigated and analysed using MATLAB and Microsoft Excel software. In the next step, the results of the three productivity measures were illustrated on separate fuzzy diagrams. The findings showed that the three measures employed could give a broader view of different aspects of derivational suffix productivity in Persian. Using fuzzy set theory makes it possible for a given suffix to belong simultaneously to different categories with different degrees of membership. According to the statistics of this research, suffixes – i and – e in Persian had the highest degrees of membership among the most productive suffixes up to now. Likewise, they continue to contribute the most to the growth rate of the contemporary Persian lexicon.","PeriodicalId":45514,"journal":{"name":"Journal of Quantitative Linguistics","volume":"29 1","pages":"387 - 411"},"PeriodicalIF":0.7000,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/09296174.2021.1887575","citationCount":"0","resultStr":"{\"title\":\"Derivational Suffix Productivity in Persian: A Fuzzy Analysis\",\"authors\":\"S. Z. Aftabi, A. Ahangar, H. M. Nehi\",\"doi\":\"10.1080/09296174.2021.1887575\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT The main aim of this article is to introduce a new way of dealing with the vague concept of suffix productivity in Persian. This approach, that is fuzzy set theory, gives each suffix a degree of membership from [0,1] to different productivity categories. To estimate morphological productivity of Persian suffixes, first Baayen’s proposed measures, i.e. realized productivity, expanding productivity and potential productivity were applied to Bijankhan corpus. Correspondingly, 2.6 million words in the corpus were investigated and analysed using MATLAB and Microsoft Excel software. In the next step, the results of the three productivity measures were illustrated on separate fuzzy diagrams. The findings showed that the three measures employed could give a broader view of different aspects of derivational suffix productivity in Persian. Using fuzzy set theory makes it possible for a given suffix to belong simultaneously to different categories with different degrees of membership. According to the statistics of this research, suffixes – i and – e in Persian had the highest degrees of membership among the most productive suffixes up to now. Likewise, they continue to contribute the most to the growth rate of the contemporary Persian lexicon.\",\"PeriodicalId\":45514,\"journal\":{\"name\":\"Journal of Quantitative Linguistics\",\"volume\":\"29 1\",\"pages\":\"387 - 411\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2021-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/09296174.2021.1887575\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Quantitative Linguistics\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1080/09296174.2021.1887575\",\"RegionNum\":2,\"RegionCategory\":\"文学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"LANGUAGE & LINGUISTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Quantitative Linguistics","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1080/09296174.2021.1887575","RegionNum":2,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
Derivational Suffix Productivity in Persian: A Fuzzy Analysis
ABSTRACT The main aim of this article is to introduce a new way of dealing with the vague concept of suffix productivity in Persian. This approach, that is fuzzy set theory, gives each suffix a degree of membership from [0,1] to different productivity categories. To estimate morphological productivity of Persian suffixes, first Baayen’s proposed measures, i.e. realized productivity, expanding productivity and potential productivity were applied to Bijankhan corpus. Correspondingly, 2.6 million words in the corpus were investigated and analysed using MATLAB and Microsoft Excel software. In the next step, the results of the three productivity measures were illustrated on separate fuzzy diagrams. The findings showed that the three measures employed could give a broader view of different aspects of derivational suffix productivity in Persian. Using fuzzy set theory makes it possible for a given suffix to belong simultaneously to different categories with different degrees of membership. According to the statistics of this research, suffixes – i and – e in Persian had the highest degrees of membership among the most productive suffixes up to now. Likewise, they continue to contribute the most to the growth rate of the contemporary Persian lexicon.
期刊介绍:
The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.