Japanese dependency structure analysis based on support vector machines

Citations Over TimeTop 10% of 2000 papers

Abstract

This paper presents a method of Japanese dependency structure analysis based on Sup-port Vector Machines (SVMs). Conventional parsing techniques based on Machine Learning framework, such as Decision Trees and Maximum Entropy Models, have difficulty in selecting useful features as well as finding appropriate combination of selected features. On the other hand, it is well-known that SVMs achieve high generalization performance even with input data of very high dimensional feature space. Furthermore, by introducing the Kernel principle, SVMs can carry out the training in high-dimensional spaces with a smaller computational cost independent of their dimensionality. We apply SVMs to Japanese dependency structure identification problem. Experimental results on Kyoto University corpus show that our system achieves the accuracy of 89.09% even with small training data (7958 sentences).

Related Papers

• High Quality Dependency Selection from Automatic Parses(2013)
• → Factors influencing dependency parsing of coordinating structure(2009)1 cited
• → A Simulated Shallow Dependency Parser Based on Weighted Hierarchical Structure Learning(2008)1 cited
• Chinese deterministic dependency analysis with consideration of long-distance dependency(2007)
• → A simulated shallow dependency parser based on weighted hierarchical structure learning(2008)