Interactive Learning of Node Selecting Tree Transducers ⋆

We develop new algorithms for learning monadic node selection queries in unranked trees from annotated examples, and apply them to visually interactive Web information extraction. We propose to represent monadic queries by bottom-up deterministic Node Selecting Tree Transducers (Nstts), a particular class of tree au-tomata that we introduce. We prove that deterministic Nstts capture the class of queries definable in monadic second order logic (Mso) in trees, which Gottlob and Koch (2002) argue to have the right expressiveness for Web information extraction, and prove that monadic queries defined by Nstts can be answered efficiently. We present a new polynomial time algorithm in Rpni-style that learns monadic queries defined by deterministic Nstts from completely annotated examples, where all selected nodes are distinguished. In practice, users prefer to provide partial annotations. We propose to account for partial annotations by intelligent tree pruning heuristics. We introduce pruning Nstts-a formalism that shares many advantages of Nstts. This leads us to an interactive learning algorithm for monadic queries defined by pruning Nstts, which satisfies a new formal active learning model in the style of Angluin (1987). We have implemented our interactive learning algorithm and integrated it into a visually interactive Web information extraction system – called Squirrel– by plugging it into the Mozilla Web browser. Experiments on ⋆ A previous version of this article was published in Machine Learning 66,1 (2007) 33–67. 2 Julien Carme et al. realistic Web documents confirm excellent quality with very few user interactions during wrapper induction.