Programming in Prolog (2nd ed.)
Programming in Prolog (2nd ed.)
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Learning Logical Definitions from Relations
Machine Learning
Predicate Invention and Learning from Positive Examples Only
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning the past tense of English verbs using inductive logic programming
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Issues in Learning Language in Logic
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Structuring Natural Language Data by Learning Rewriting Rules
Inductive Logic Programming
Extraction of genic interactions with the recursive logical theory of an ontology
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Multi model transfer learning with RULES family
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Transfer rules are used in bi-lingual translation systems for transferring a logical representation of a source language sentence into a logical representation of the corresponding target language sentence. This work studies induction of transfer rules from examples of corresponding pairs of source-target quasi logical formulae (QLFs). The main features of this problem are: i) more than one rule may need to be produced from a single example, ii) only positive examples are provided and iii) the produced hypothesis should be recursive. In an earlier study of this problem, a system was proposed in which hand-coded heuristics were employed for identifying non-recursive correspondences. In this work we study the case when non-recursive transfer rules have been given to the system instead of heuristics. Results from a preliminary experiment with English-French QLFs are presented, demonstrating that this information is sufficient for the generation of generally applicable rules that can be used for transfer between previously unseen source and target QLFs. However, the experiment also shows that the system suffers from producing overly specific rules, even when the problem of disallowing the derivation of other target QLFs than the correct one is not considered. Potential approaches to this problem are discussed.