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Learning Information Extraction Rules: An Inductive Logic Programming approach

Summary: Learning Information Extraction Rules:
An Inductive Logic Programming approach
James Stuart Aitken

The objective of this work is to learn information extraction rules
by applying Inductive Logic Programming (ILP) techniques to natu-
ral language data. The approach is ontology-based, which means that
the extraction rules conclude with specific ontology relations that
characterise the meaning of sentences in the text. An existing ILP
system, FOIL, is used to learn attribute-value relations. This enables
instances of these relations to be identified in the text. In specific,
we explore the linguistic preprocessing of the data, the use of back-
ground knowledge in the learning process, and the practical consid-
erations of applying a supervised learning approach to rule induction,
i.e. in terms of the human effort in creating the data set, and in the
inherent biases in the use of small data sets.
1 Introduction
Automatically deriving a semantic interpretation of free text is a chal-
lenging research task [11, 12] which has an immediate and pressing


Source: Aitken, Stuart - Artificial Intelligence Applications Institute, School of Informatics, University of Edinburgh


Collections: Computer Technologies and Information Sciences