{"title":"可读性分析的过去、问题和潜力","authors":"Nicholas A. Lines","doi":"10.1080/09332480.2022.2066411","DOIUrl":null,"url":null,"abstract":"Readability analysis combines statistical modeling, theoretical linguistics, and psychological theory to determine the accessibility level of writing samples. This study has a long history and broad impact, yet typically uses extremely simple statistical tools (in particular linear regressions). This article briefly reviews key stages in the history of readability, and discusses present issues and potential future benefits these tools offer.","PeriodicalId":88226,"journal":{"name":"Chance (New York, N.Y.)","volume":"137 1","pages":"16 - 24"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Past, Problems, and Potential of Readability Analysis\",\"authors\":\"Nicholas A. Lines\",\"doi\":\"10.1080/09332480.2022.2066411\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Readability analysis combines statistical modeling, theoretical linguistics, and psychological theory to determine the accessibility level of writing samples. This study has a long history and broad impact, yet typically uses extremely simple statistical tools (in particular linear regressions). This article briefly reviews key stages in the history of readability, and discusses present issues and potential future benefits these tools offer.\",\"PeriodicalId\":88226,\"journal\":{\"name\":\"Chance (New York, N.Y.)\",\"volume\":\"137 1\",\"pages\":\"16 - 24\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chance (New York, N.Y.)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/09332480.2022.2066411\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chance (New York, N.Y.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/09332480.2022.2066411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Past, Problems, and Potential of Readability Analysis
Readability analysis combines statistical modeling, theoretical linguistics, and psychological theory to determine the accessibility level of writing samples. This study has a long history and broad impact, yet typically uses extremely simple statistical tools (in particular linear regressions). This article briefly reviews key stages in the history of readability, and discusses present issues and potential future benefits these tools offer.
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