A maximum entropy model for parsing

this paper, we present a method where more of the tree structure is used in the parsing model. We define a set of features that capture long distance dependency such as parallelism in coordination. These features are then integrated with a Maximum Entropy model into an overall probabilistic model for parsing. We introduce the decision tree parser in Section 2, describe the Maximum Entropy model in Section 3, describe the feature extraction algorithm in Section 4, give experimental results in Section 5, and present our conclusions in Section 6.