Learning Stochastic Tree Edit Distance

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Learning Stochastic Tree Edit Distance
Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance (ED) for which improvements in terms of complexity have been achieved during the last decade. However, this classic ED usually uses a priori fixed edit costs which are often difficult to tune, that leaves little room for tackling complex problems. In this paper, we focus on the learning of a stochastic tree ED. We use an adaptation of the ExpectationMaximization algorithm for learning the primitive edit costs. We carried out series of experiments that confirm the interest to learn a tree ED rather than a priori imposing edit costs. Keywords. Stochastic tree edit distance, EM algorithm, generative models, discriminative models.
Marc Bernard, Amaury Habrard, Marc Sebban
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where ECML
Authors Marc Bernard, Amaury Habrard, Marc Sebban
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