Using Decision Trees to Construct a Practical Parser

Abstract

This paper describes a novel and practical Japanese parser 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 constructed parsers are evaluated using the EDR Japanese annotated corpus. The single-tree method significantly outperforms the conventional Japanese stochastic methods. Moreover, the boosted version of the parser is shown to have great advantages; (1) a better parsing accuracy than its single-tree counterpart for any amount of training data and (2) no over-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 and machine learning.