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Based on this dataset, we conduct numerous experiments for the task of ancient Tibetan word segmentation. Experimental results show that BiLSTM + CRF word segmentation algorithm can achieve the best performance, and the performance of ancient Tibetan word segmentation can be further improved through model ensemble. And the results show that the unknown words, insufficient training data and word ambiguity restrict the performance of ancient Tibetan word segmentation. <\/jats:p>","DOI":"10.1142\/s2717554522500047","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T02:08:02Z","timestamp":1647828482000},"source":"Crossref","is-referenced-by-count":0,"title":["Ancient Tibetan Word Segmentation: Dataset and Methods"],"prefix":"10.1142","volume":"31","author":[{"given":"Bo","family":"An","sequence":"first","affiliation":[{"name":"The Institute of Ethnology and Anthropology, Chinese Academy of Social Sciences, Beijing, P. R. 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