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This paper proposes a Tibetan word segmentation method based on bidirectional long-term memory neural network with conditional random field model (BiLSTM_ CRF).
We propose a Tibetan word segmentation method based on CNN-BiLSTM-CRF model that merely uses the characters of sentence as the input so that the method does ...
The experimental results show that the proposed Tibetan word segmentation method based on bidirectional long-term memory neural network with conditional ...
We propose a Tibetan word segmentation method based on CNN-BiLSTM-CRF model that merely uses the characters of sentence as the input so that the method does ...
... The reason for inserting a CRF layer into the BiLSTM network is to standardise the LSTM network input and prevent illegal labelling [25] . Although the ...
According to the characteristics of Tibetan named entity itself, this paper analyzes the text characteristics and recognition difficulties of named entity, ...
Based on this dataset, we conduct numerous experiments for the task of ancient Tibetan word segmentation. Experimental results show that BiLSTM + CRF word ...
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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 experimental results demonstrate that this model is able to recognize 10 kinds of labels in the dataset. Moreover, the recognition effect of each label has ...
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 ...