Paper: Making Tree Kernels Practical For Natural Language Learning

ACL ID E06-1015
Title Making Tree Kernels Practical For Natural Language Learning
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

In recent years tree kernels have been pro- posed for the automatic learning of natural language applications. Unfortunately, they show (a) an inherent super linear complex- ity and (b) a lower accuracy than tradi- tional attribute/value methods. In this paper, we show that tree kernels are very helpful in the processing of nat- ural language as (a) we provide a simple algorithm to compute tree kernels in linear average running time and (b) our study on the classification properties of diverse tree kernels show that kernel combinations al- ways improve the traditional methods. Ex- periments with Support Vector Machines on the predicate argument classification task provide empirical support to our the- sis.