Learning Node Selecting Tree Transducer from Completely Annotated Examples

Abstract. A base problem in Web information extraction is to find appropriate queries for informative nodes in trees. We propose to learn queries for nodes in trees automatically from examples. We introduce node selecting tree transducer (NSTT) and show how to induce deterministic NSTTs in polynomial time from completely annotated examples. We have implemented learning algorithms for NSTTs, started applying them to Web information extraction, and present first experimental results.

• Type: Conference
• Year: 2004
• Where: ICGI
• Authors: Julien Carme, Aurélien Lemay, Joachim Niehren
• Keywords: ICGI 2004, Informative Nodes, Natural Language Processing, Selecting Tree Transducer, Web Information Extraction