基于概率决策树的句法分析--以名词性补语从句和关系从句中的后置“that”为例
Fine-tuning a Subtle Parsing Distinction Using a Probabilistic Decision Tree: the Case of Postnominal "that" in Noun Complement Clauses vs. Relative Clauses
摘要：
本文研究了英语关系和名词性补足语从句的两种不同的句法分析方法，并对它们的对应关系代词和补足语使用了不同的标记。我们使用了一个算法来重新标记一个语料库解析与GUM树库使用通用依赖。我们的第二个实验包括使用TreeTagger，一种概率决策树，来学习名词性后“that”的两种补语和相对用法之间的区别。我们研究了训练集大小对TreeTagger准确性的影响，以及GUM Treebank文件对两种结构的代表性。我们讨论了这种区别的可学习性的一些语言和结构原则。
英文摘要：
In this paper we investigated two different methods to parse relative and noun complement clauses in English and resorted to distinct tags for their corresponding that as a relative pronoun and as a complementizer. We used an algorithm to relabel a corpus parsed with the GUM Treebank using Universal Dependency. Our second experiment consisted in using TreeTagger, a Probabilistic Decision Tree, to learn the distinction between the two complement and relative uses of postnominal "that". We investigated the effect of the training set size on TreeTagger accuracy and how representative the GUM Treebank files are for the two structures under scrutiny. We discussed some of the linguistic and structural tenets of the learnability of this distinction.
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