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Using decision trees to construct a practical parser
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DOI：10.1023/A:1007597902467.png)
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Abstract
En 中文
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.
Keywords:
stochastic parsing
decision tree
boosting
dependency grammar
corpus linguistics
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