Abstract
Tibetan word segmentation is the primary task of various types of Tibetan natural language processing. However, when the test corpus and the training corpus are inconsistent, the accuracy of word segmentation will drop significantly. Therefore, in this paper firstly we build a BiLSTM-CRF Tibetan word segmentation model based on deep learning that includes dictionary features, and train the model parameters on the open domain word segmentation data. Then, taking the Tibetan medical clinical text as a small-scale word segmentation training corpus, and fine-tuning the parameters of the BiLSTM-CRF word segmentation model of the open domain corpus, and adding the domain vocabulary to the dictionary features of the model. So that the precision, recall rate and F value of the word segmentation model on the test data reached 93.21%, 92.10%, and 94.37% respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
