Conversion of Japanese passive/causative sentences into active sentences using machine learning

Authors:
Masaki Murata;Hitoshi Isahara
Affiliations:
Communications Research Laboratory, Kyoto, Japan;Communications Research Laboratory, Kyoto, Japan
Venue:
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Year:
2003

Citing 5
Cited 0

Feature selection in SVM text categorization

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Improving accuracy in word class tagging through the combination of machine learning systems

Computational Linguistics
Use of support vector learning for chunk identification

ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Using a support-vector machine for Japanese-to-English translation of tense, aspect, and modality

DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14

Quantified Score

Hi-index	0.00

Visualization

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.