Proceedings:
Proceedings of the AAAI Conference on Artificial Intelligence, 16
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 16
Track:
Learning
Downloads:
Abstract:
Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. This paper presents a system, RAPIER, that takes pairs of sample documents and filled templates and induces pattern-match rules that directly extract fillers for the slots in the template. RAPIER employs a bottom-up learning algorithm which incorporates techniques from several inductive logic programming systems and acquires unbounded patterns that include constraints on the words, part-of-speech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains.
![](https://aaai.org/wp-content/uploads/2023/04/aaai99.jpg)
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 16
ISBN 978-0-262-51106-3
July 18-22, 1999, Orlando, Florida. Published by The AAAI Press, Menlo Park, California.