Paper: Japanese Dependency Structure Analysis Based On Support Vector Machines

ACL ID W00-1303
Title Japanese Dependency Structure Analysis Based On Support Vector Machines
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 2000
Authors

This paper presents a method of Japanese dependency structure analysis based on Sup-- port Vector Machines (SVMs). Conventional parsing techniques based on Machine Learn- ing framework, such as Decision Trees and Maximum Entropy Models, have difficulty in selecting useful features as well as find- ing appropriate combination of selected fea- tures. On the other hand, it is well-known that SVMs achieve high generalization per- formance 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 in- dependent of their dimensionality. We apply SVMs to Japanese dependency structure iden- tification problem. Experimental results on Kyoto University ...