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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,1,31]]},"abstract":"<jats:p>This article presents a pipeline that converts collections of Tibetan documents in plain text or XML into a fully segmented and POS-tagged corpus. We apply the pipeline to the large extent collection of the Buddhist Digital Resource Center. The semi-supervised methods presented here not only result in a new and improved version of the largest annotated Tibetan corpus to date, the integration of rule-based, memory-based, and neural-network methods also serves as a good example of how to overcome challenges of under-researched languages. The end-to-end accuracy of our entire automatic pipeline of 91.99% is high enough to make the resulting corpus a useful resource for both linguists and scholars of Tibetan studies.<\/jats:p>","DOI":"10.1145\/3409488","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T13:13:05Z","timestamp":1615381985000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Optimisation of the Largest Annotated Tibetan Corpus Combining Rule-based, Memory-based, and Deep-learning Methods"],"prefix":"10.1145","volume":"20","author":[{"given":"Marieke","family":"Meelen","sequence":"first","affiliation":[{"name":"University of Cambridge"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c9lie","family":"Roux","sequence":"additional","affiliation":[{"name":"BDRC"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathan","family":"Hill","sequence":"additional","affiliation":[{"name":"SOAS, University of London"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-3031"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.26615\/978-954-452-056-4_035"},{"key":"e_1_2_1_4_1","first-page":"9","article-title":"A rule-based part-of-speech tagger for Classical Tibetan","volume":"13","author":"Garrett Edward","year":"2014","journal-title":"Himal. 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A New Perspective for Modern Tibetan Machine Processing and its Development: An Insight Into the Method of Computerized Automatic Understanding of Natural Languages in Terms of Chunk Identification. \u79d1\u5b66\u51fa\u7248\u793e Kexue Chubanshe 438--448.  Di Jiang. 2003. A New Perspective for Modern Tibetan Machine Processing and its Development: An Insight Into the Method of Computerized Automatic Understanding of Natural Languages in Terms of Chunk Identification. \u79d1\u5b66\u51fa\u7248\u793e Kexue Chubanshe 438--448."},{"volume-title":"Proceedings of the International Conference on Asian Language Processing. 239--242","year":"2013","author":"Kang C.","key":"e_1_2_1_11_1"},{"volume-title":"Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data","author":"Liu Huidan","key":"e_1_2_1_12_1"},{"key":"e_1_2_1_13_1","first-page":"19","article-title":"Fusion of word clustering features for Tibetan part of speech tagging based on maximum entropy model","volume":"17","author":"Ma Ning","year":"2016","journal-title":"Int. J. Simul. Syst. Sci. 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