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Journal Articles Machine Learning Year : 2007

Interactive Learning of Node Selecting Tree Transducers

Abstract

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 NNSTs, a particular class of tree automata that we introduce. We prove that deterministic NNSTs 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 NNSTs can be answered efficiently. We present a new polynomial time algorithm in RPNI-style that learns monadic queries defined by deterministic NNSTs 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 (1887). 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 realistic Web documents confirm excellent quality with very few user interactions during wrapper induction.
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Dates and versions

inria-00087226 , version 1 (21-07-2006)
inria-00087226 , version 2 (03-08-2006)
inria-00087226 , version 3 (17-11-2008)
inria-00087226 , version 4 (09-03-2010)
inria-00087226 , version 5 (09-04-2010)

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Cite

Julien Carme, Rémi Gilleron, Aurélien Lemay, Joachim Niehren. Interactive Learning of Node Selecting Tree Transducers. Machine Learning, 2007, Machine Learning, 66 (1), pp.33-67. ⟨10.1007/s10994-006-9613-8⟩. ⟨inria-00087226v5⟩
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