This paper describes novel and practical Japanese parsers that uses decision trees. First, we construct a single decision tree to estimate modification probabilities; how one phrase tends to modify another. Next, we introduce a boosting algorithm in which several decision trees are constructed and then combined for probability estimation. The two constructed parsers are evaluated by using the EDR Japanese annotated corpus. The single-tree method outperforms the conventional Japanese stochastic methods by 4%. Moreover, the boosting version is shown to have significant advantages; 1) better parsing accuracy than its single-tree counterpart for any amount of training data and 2) no over-fitting to data for various iterations.

Wc use seven machine learning algorithms one t;sk: idenl, it)ing base nom phrases. The results have been processed by (lifin'ent system confi)ination methods and all of these outpertbrmed the best individual result. Wc lmw applied the sewm learners wil, h tim best, combinatot, a majo1'it,y vote of the 1,o t) five sysl,elnS, to a sta.l(lard (bt; sol, and mmaged lt) ilnl)rove the t)cst published resull; tbr this (lata set.