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This historically unprecedented surge\u2014especially over the past 200 years\u2014has changed nearly everything about the ancient technology of reading.\n            <jats:italic>Who<\/jats:italic>\n            reads is changing: Literacy is no longer just for elite, professional readers, but for anyone and everyone.\n            <jats:italic>What<\/jats:italic>\n            and\n            <jats:italic>why<\/jats:italic>\n            we read is changing: We do not just read\n            <jats:italic>difficult<\/jats:italic>\n            texts for academic, religious, legal, or record-keeping purposes; we also read\n            <jats:italic>easy<\/jats:italic>\n            texts to be entertained, to access information, and to communicate with each other on a daily basis. And\n            <jats:italic>how<\/jats:italic>\n            we read is changing: Memorization, recitation, and oral performance has given way to a rapid, silent, individual activity.\n          <\/jats:p>\n          <jats:p>\n            Many of these democratizing changes have been made possible by technology. This has included advances in methods and materials that have made reading and writing easy, cheap, and widely available\u2014like paper, the printing press, and the digital revolution. But perhaps the biggest reason literacy has become so widespread has been its ability to reach people\n            <jats:italic>in their own natural languages<\/jats:italic>\n            . More recently, this progress has been enhanced by NLP tools, like readability editors, that have helped authors, journalists, and other writing professionals make simple, clear content suitable for both beginning readers and widespread audiences.\n          <\/jats:p>\n          <jats:p>To that end, this article introduces a new readability tool, \u201cDakje,\u201d alongside a specific use case, and demonstrates how it can help benefit literacy in the Tibetan languages. This NLP software works by word-splitting Tibetan text and analyzing those words using level lists that are based on frequency analysis from corpora. Users then have instant access to statistics on the readability of their word choices so they can make edits for easy-to-read text. In our test-case, Dakje helped us reduce sentence complexity by 34%, total word count by 10%, and non-level vocabulary use from 16% to 1% when compared to an original English-to-Tibetan translation.<\/jats:p>","DOI":"10.1145\/3392046","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:39:07Z","timestamp":1594125547000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Grading Tibetan Children\u2019s Literature"],"prefix":"10.1145","volume":"19","author":[{"given":"Dirk","family":"Schmidt","sequence":"first","affiliation":[{"name":"R8D, Esukhia, Dharamsala, KANGRA HP, Himachal Pradesh, India"}]}],"member":"320","published-online":{"date-parts":[[2020,10,6]]},"reference":[{"unstructured":"Charles A. Perfetti and Susan Dunlap. 2008. Learning to read across languages: Cross-linguistic relationships in first and second language literacy development. 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