Paper: Machine-Learning-Based Transformation Of Passive Japanese Sentences Into Active By Separating Training Data Into Each Input Particle

ACL ID P06-2076
Title Machine-Learning-Based Transformation Of Passive Japanese Sentences Into Active By Separating Training Data Into Each Input Particle
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

We developed a new method of transform- ing Japanese case particles when trans- forming Japanese passive sentences into active sentences. It separates training data into each input particle and uses machine learning for each particle. We also used numerous rich features for learning. Our method obtained a high rate of accuracy (94.30%). In contrast, a method that did not separate training data for any input particles obtained a lower rate of accu- racy (92.00%). In addition, a method that did not have many rich features for learning used in a previous study (Mu- rata and Isahara, 2003) obtained a much lower accuracy rate (89.77%). We con- firmed that these improvements were sig- nificant through a statistical test. We also conducted experiments utilizing tra- ditional methods using verb di...