当前位置：
X-MOL 学术
›
ACM Trans. Asian Low Resour. Lang. Inf. Process.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Optimisation of the Largest Annotated Tibetan Corpus Combining Rule-based, Memory-based, and Deep-learning Methods
ACM Transactions on Asian and Low-Resource Language Information Processing ( IF 2 ) Pub Date : 2021-03-10 , DOI: 10.1145/3409488 Marieke Meelen 1 , Élie Roux 2 , Nathan Hill 3
ACM Transactions on Asian and Low-Resource Language Information Processing ( IF 2 ) Pub Date : 2021-03-10 , DOI: 10.1145/3409488 Marieke Meelen 1 , Élie Roux 2 , Nathan Hill 3
Affiliation
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.
更新日期：2021-03-10
京公网安备 11010802027423号