Improved parsing with taxonomy of conjunctions

Dongchen Li, Xiantao Zhang, Xihong Wu
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引用次数: 1

Abstract

Incorporating knowledge for training a parser has been shown to remedy the weaknesses of probabilistic context-free grammar. Previous parsing systems have exploited content words semantic resource and word-formation knowledge. However, they are limited in that they do not take into account conjunction category refinement, which stands out to be helpful in predicting the syntactic structure and syntactic label in Chinese. We define a conjunction taxonomy representing intrinsic syntactic constraints, and show that refined categories in the taxonomy for conjunctions contribute to improved parsing performance. The taxonomy is used to supervise the splitting of these refined tags, and the automatic hierarchical state-split approach is employ to compensate the limitation in the scope and refinement degree of the taxonomy. The experiments are carried out on Penn Chinese Treebank, which show that our method can improve parsing performance significantly.
改进了连词分类的解析
结合训练解析器的知识已被证明可以弥补概率上下文无关语法的弱点。以往的句法分析系统主要利用实词语义资源和构词知识。然而,它们的局限性在于没有考虑连接范畴的细化,而连接范畴的细化在预测汉语句法结构和句法标签方面具有突出的作用。我们定义了一个表示内在语法约束的连接分类法,并证明了连接分类法中精炼的类别有助于提高解析性能。该分类法用于监督这些精细标签的分割,并采用自动分层状态分割方法来弥补分类法在范围和精细程度上的局限性。在宾夕法尼亚大学中文树库上进行了实验,结果表明该方法可以显著提高解析性能。
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