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If the prediction succeeds, the user does not need to consult a dictionary; even if it fails, the user can correct the prediction. The correction data are collected and used to improve the accuracy of further predictions. The prediction is personalized in that every user's language ability is estimated by a state-of-the-art language testing model, which is trained in a practical response time with only a small sacrifice of prediction accuracy. The system was evaluated in terms of prediction accuracy and reading simulation. The reading simulation results show that this system can reduce the number of clicks for most readers with insufficient vocabulary to read documents and can significantly reduce the remaining number of unfamiliar words after the prediction and glossing for all users.<\/jats:p>","DOI":"10.1145\/2438653.2438666","type":"journal-article","created":{"date-parts":[[2013,4,1]],"date-time":"2013-04-01T19:39:32Z","timestamp":1364845172000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Personalized reading support for second-language web documents"],"prefix":"10.1145","volume":"4","author":[{"given":"Yo","family":"Ehara","sequence":"first","affiliation":[{"name":"The University of Tokyo, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nobuyuki","family":"Shimizu","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takashi","family":"Ninomiya","sequence":"additional","affiliation":[{"name":"Ehime University, Ehime, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Nakagawa","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2013,4,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Baker F. and Kim S. 2004. 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