Abstract
Uyghur is an agglutinative language that has many morphemes. It is necessary for processing Uyghur to segment words into morphemes. This work is called morphological segmentation. Previous works treat morphological segmentation as a tagging task and classify each character as one of four classes, which are \(\{b,m,e,s\}\). However, these labels are not independent from each other, which makes the models easily overfitted. We propose a new method for the segmentation task. Instead of using these labels, we use only segmentation points for modeling. The model used in our method is more robust and easier to train than previous methods. Applying our model to Uyghur morphological segmentation, it achieves high accuracy and higher recall and f1 score than previous models.
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Change history
13 October 2019
The original version of this chapter contained an error in the second author’s name. The spelling of Shuqin Li’s name was incorrect in the header of the paper. The author’s name has been corrected.
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Acknowledgements
This work was supported by National Science Foundation of China (Grant No. 61772075), National Science Foundation of China (Grant No. 61772081), Scientific Research Project of Beijing Educational Committee (Grant No. KM201711232022), Beijing Municipal Education Committee (Grant No. SZ20171123228), Beijing Institute of Computer Technology and Application (Grant by Extensible Knowledge Graph Construction Technique Project).
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Yang, Y., Li, S., Zhang, Y., Zhang, HP. (2019). Point the Point: Uyghur Morphological Segmentation Using PointerNetwork with GRU. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science(), vol 11856. Springer, Cham. https://doi.org/10.1007/978-3-030-32381-3_30
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