Abstract
We developed a new method of machine learning for converting Japanese case-marking particles when converting Japanese passive/ causative sentences into active sentences. Our method has an accuracy rate of 89.06% for normal supervised learning. We also developed a new method of using the results of unsupervised learning as features for supervised learning and obtained a slightly higher accuracy rate (89.55%). We confirmed by using a statistical test that this improvement is significant.
In this study, we do not handle the conversion of the expression of the auxiliary verb because auxiliary verbs can be converted based on the Japanese grammar.
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Murata, M., Isahara, H. (2003). Conversion of Japanese Passive/Causative Sentences into Active Sentences Using Machine Learning. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2003. Lecture Notes in Computer Science, vol 2588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36456-0_12
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DOI: https://doi.org/10.1007/3-540-36456-0_12
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