tailieunhanh - Báo cáo khoa học: "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%. . | Using Decision Trees to Construct a Practical Parser Masahiko Haruno Satoshi ShiraH Yoshifumi Ooyamat mharuno@ shirai@ ooyama@ ATR Human Information Processing Research Laboratories 2-2 Hikaridai Seika-cho Soraku-gun Kyoto 619-02 Japan. NTT Communication Science Laboratories 2-4 Hikaridai Seika-cho Soraku-gun Kyoto 619-02 Japan. Abstract 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. 1 Introduction Conventional parsers with practical levels of performance require a number of sophisticated rules that have to be hand-crafted by human linguists. It is time-consuming and cumbersome to maintain these rules for two reasons. The rules are specific to the application domain. Specific rules handling collocational expressions create side effects. Such rules often deteriorate the overall performance of the parser. The stochastic approach on the other hand has the potential to overcome these difficulties. Because it induces stochastic rules to maximize overall performance against training data it not only adapts to any application domain but also may avoid overfitting to the data. In the late 80s and early 90s the induction and parameter estimation of probabilistic context free grammars PCFGs from corpora were intensively studied. Because these grammars .