| Autor: |
Haruno, Masahiko, Shirai, Satoshi, Ooyama, Yoshifumi |
| Zdroj: |
Machine Learning; Feb1999, Vol. 34 Issue 1-3, p131-149, 19p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink