Learning semantic-level information extraction rules by type-oriented ILP
Yutaka Sasaki, Yoshihiro Matsuo · 2000
This paper describes an approach to using semantic representations for learning information extraction (IE) rules by a type-oriented inductive logic programming (ILP) system. NLP components of a machine translation system are used to automatically generate semantic representations of text corpus that can be given directly to an ILP system. The latest experimental results show high precision and recall of the learned rules.