Learning n-Ary Node Selecting Tree Transducers from Completely Annotated Examples

We present the first algorithm for learning n-ary node selection queries in trees from completely annotated examples by methods of grammatical inference. We propose to represent n-ary queries by deterministic n-ary node selecting tree transducers (n-NSTTs). These are tree automata that capture the class of monadic second-order definable nary queries. We show that n-NSTT defined polynomially bounded n-ary queries can be learned from polynomial time and data. An application in Web information extraction yields encouraging results.