Paper: Using Decision Trees to Construct a Practical Parser

ACL ID P98-1083
Title Using Decision Trees to Construct a Practical Parser
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1998
Authors

This paper describes novel and practical Japanese parsers that uses decision trees. First, we con- struct a single decision tree to estimate modifica- tion 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 pars- ing accuracy than its single-tree counterpart for any amount of training data and 2) no over-fitting to data for various iterations.