达到最大解析精度的最小片段集是什么？
What is the minimal set of fragments that achieves maximal parse accuracy?
摘要：
我们的目标是找到最小的片段集，实现最大的分析精度在面向数据的分析。宾夕法尼亚华尔街日报树库的实验表明，解析树中几乎任意片段的计数是重要的，这导致了比以前在该树库上测试的模型更高的解析精度（90.8%的精度和90.6%的召回率）。我们隔离一些依赖关系，以前的模型忽略，但有助于更高的解析精度。
英文摘要：
We aim at finding the minimal set of fragments which achieves maximal parse accuracy in Data Oriented Parsing. Experiments with the Penn Wall Street Journal treebank show that counts of almost arbitrary fragments within parse trees are important, leading to improved parse accuracy over previous models tested on this treebank (a precision of 90.8% and a recall of 90.6%). We isolate some dependency relations which previous models neglect but which contribute to higher parse accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
