Abstract. In this paper, we explored a learning approach which com-bines dierent learning methods in inductive logic programming (ILP) to allow a learner to produce more expressive hypotheses 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 classi-cation problems and each has its own characteristics which may or may not t a particular learning method. The task of semantic parser acqui-sition in two dierent domains was attempted and preliminary results demonstrated that such an approach is promising.
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a...
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic progra...
Inductive Logic Programming (ILP) is concerned with learning relational descriptions that typically...
In this paper, we explored a learning approach which combines different learning methods in inducti...
Empirical methods for building natural language systems has become an important area of research in ...
AbstractInductive Logic Programming (ILP) is the area of AI which deals with the induction of hypoth...
textInductive Logic Programming (ILP) is the intersection of Machine Learning and Logic Programming...
We summarise recent work on using Inductive Logic Programming (ILP) for Natural Language Processing ...
Acquiring and maintaining Semantic Web rules is very demanding and can be automated though partially...
Inductive Logic Programming (ILP) is a form of machine learning. It is an approach to machine learni...
Automating the construction of semantic grammars is a di cult and interesting problem for machine le...
. This paper presents results from recent experiments with Chill, a corpus-based parser acquisition...
We present an approach for solving some of the problems of top-down Inductive Logic Programming sys...
. This paper reviews our recent work on applying inductive logic programming to the construction of ...
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a...
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a...
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic progra...
Inductive Logic Programming (ILP) is concerned with learning relational descriptions that typically...
In this paper, we explored a learning approach which combines different learning methods in inducti...
Empirical methods for building natural language systems has become an important area of research in ...
AbstractInductive Logic Programming (ILP) is the area of AI which deals with the induction of hypoth...
textInductive Logic Programming (ILP) is the intersection of Machine Learning and Logic Programming...
We summarise recent work on using Inductive Logic Programming (ILP) for Natural Language Processing ...
Acquiring and maintaining Semantic Web rules is very demanding and can be automated though partially...
Inductive Logic Programming (ILP) is a form of machine learning. It is an approach to machine learni...
Automating the construction of semantic grammars is a di cult and interesting problem for machine le...
. This paper presents results from recent experiments with Chill, a corpus-based parser acquisition...
We present an approach for solving some of the problems of top-down Inductive Logic Programming sys...
. This paper reviews our recent work on applying inductive logic programming to the construction of ...
When comparing inductive logic programming (ILP) and attribute-value learning techniques, there is a...
Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a...
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic progra...
Inductive Logic Programming (ILP) is concerned with learning relational descriptions that typically...