Proceedings of the 12th European Conference on Machine Learning (ECML-2001), pp. 466-477, Freiburg, Germany, September 2001.
Paper ID: 107
Category: Inductive Logic Programming, Natural Language Learning, Learning for Semantic Parsing
In this paper, we explored a learning approach which combines different learning methods in inductive logic programming (ILP) to allow a learner to produce more expressive hypothese than that of each individual learner. Such a learning approach may be useful when the performance of the task depends on solving a large amount of classification problems and each has its own characteristics which may or may not fit a particular learning method. The task of sematnic parser acquisition in two different domains was attempted and preliminary results demonstrated that such an approach is promising.
