Abstract
For the difficulty of marking Vietnamese dependency tree, this paper proposed the method which combined MST algorithm and improved Nivre algorithm to build Vietnamese dependency treebank. The method took full advantage of the characteristics of collaborative training. Firstly, we built a bit samples. Secondly, we used the samples to build two weak learners with two fully redundant views. Then, we marked a large number of unmarked samples mutually. Next, we selected the samples of high trust to relearn and built a dependency parsing system. Finally, we used 5000 Vietnamese sentences marked manually to do tenfold cross-test and obtained the accuracy of 76.33 %. Experimental results showed that the proposed method in this paper could take full advantage of unmarked corpus to effectively improve the quality of dependency treebank.
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61262041, 61363044 and 61472168) and the key project of National Natural Science Foundation of Yunnan province (Grant No. 2013FA030).
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Qiu, G., Guo, J., Yu, Z., Xian, Y., Mao, C. (2016). Using Collaborative Training Method to Build Vietnamese Dependency Treebank. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_8
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