Abstract
The paper introduces an input-driven generative model for tree-structured data that extends the bottom-up hidden tree Markov model to non-homogeneous state transition and emission probabilities. We show how the proposed input-driven approach can be used to realize different types of structured transductions between trees. A thorough experimental analysis is proposed to investigate the advantage of introducing an input-driven dynamics in structured-data processing. The results of this analysis suggest that input-driven models can capture more discriminative structural information than homogeneous approaches in computational learning tasks, including document classification and more general substructure categorization.
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