A Bottom-up Hidden Tree Markov Model

Bacciu, Davide and Micheli, Alessio and Sperduti, Alessandro (2010) A Bottom-up Hidden Tree Markov Model. Technical Report del Dipartimento di Informatica . Università di Pisa, Pisa, IT.

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Abstract

Hidden Tree Markov Models describe probability distributions over tree-structured data by defining a top-down generative process from the root to the leaves of the tree. We provide a novel compositional hidden tree Markov model that inverts the generative process, allowing hidden states to better correlate and model the co-occurrence of substructures among the child subtrees of internal nodes. To this end, we introduce a mixed memory approximation that factorizes the joint children-to-parent state transition matrix as a mixture of pairwise transitions. This Technical Report provides and in-depth introduction to the Bottom-Up Hidden Tree Markov Model, including the details of the learning and inference procedures.

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