Recently, a model for supervised learning of probabilistic transducers represented by suffix trees was introduced. However, this algorithm tends to build very large trees, requiring very large amounts of computer memory. In this paper, we propose anew, more compact, transducer model in which one shares the parameters of distributions associated to contexts yielding similar conditional output distributions. We illustrate the advantages of the proposed algorithm with comparative experiments on inducing a noun phrase recogmzer.

Recently, a model for supervised learning of probabilistic transducers representedby suffix trees was introduced. However, this algorithm tendsto build very large trees, requiring very large amounts of computer memory. In this paper, we propose anew, more compact, transducermodel in which one shares the parameters of distributions associatedto contexts yielding similar conditional output distributions. We illustrate the advantages of the proposed algorithm withcomparative experiments on inducing a noun phrase recogmzer.