| Issue |
MATEC Web Conf.
Volume 336, 2021
2020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|
|
|---|---|---|
| Article Number | 06018 | |
| Number of page(s) | 9 | |
| Section | Artificial Recognition and Application | |
| DOI | https://doi.org/10.1051/matecconf/202133606018 | |
| Published online | 15 February 2021 | |
Transition based neural network dependency parsing of Tibetan
1 College of Computer Science and Technology, Qinghai Normal University, Qinghai Xining 810016
2 Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province, Qinghai Xining 810008
3 Key Laboratory of Tibetan Information Processing, Ministry of Education, Qinghai Xining 810008
* Corresponding author: duojiecairang0626@qq.com
In order to improve the performance of Tibetan natural language processing applications such as machine translation, sentiment analysis and other tasks, this article proposes a neural network-based method for syntactic analysis of Tibetan language dependence. Part of the corpus of Qinghai Normal University’s part-of-speech tag set is marked by the corresponding mapping relationship is transformed into the corpus annotated by the national standard part-of-speech tag set. At the same time, the CoNLL format Tibetan language dependency syntax tree library is constructed, and the method of shift-reduce plus neural network is adopted to systematically study and analyze the Tibetan language dependency syntax. Thereby improving the quality of Tibetan dependency syntactic analysis, and its accuracy rate reaches UAS:94.59%
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.