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ACL
1998

Using Decision Trees to Construct a Practical Parser

8 years 8 months ago
Using Decision Trees to Construct a Practical Parser
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.
Masahiko Haruno, Satoshi Shirai, Yoshifumi Ooyama
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where ACL
Authors Masahiko Haruno, Satoshi Shirai, Yoshifumi Ooyama
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