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This paper describes a novel and practical Japaneseparser that uses decision trees. First, we construct a single decision treeto estimate modification probabilities; how one phrase tends tomodify another. Next, we introduce a boosting algorithm in whichseveral decision trees are constructed and then combined forprobability estimation. The constructed parsers are evaluated usingthe EDR Japanese annotated corpus. The single-tree methodsignificantly outperforms the conventional Japanese stochasticmethods. Moreover, the boosted version of the parser is shown to have great advantages; (1) a better parsing accuracy than itssingle-tree counterpart for any amount of training data and (2) noover-fitting to data for various iterations. The presented parser,the first non-English stochastic parser with practical performance,should tighten the coupling between natural language processing andmachine learning.

Using Decision Trees to Construct a Practical Parser