Left-corner Parsing for Dependency Grammar

Q4 Computer Science
Hiroshi Noji, Yusuke Miyao
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引用次数: 4

Abstract

In this article, we present an incremental dependency parsing algorithm with an arc-eager variant of the left-corner parsing strategy. Our algorithm’s stack depth captures the center-embeddedness of the recognized dependency structure. A higher stack depth occurs only when processing deeper center-embedded sentences in which people find difficulty in comprehension. We examine whether our algorithm can capture the syntactic regularity that universally exists in languages through two kinds of experiments across treebanks of 19 languages. We first show through oracle parsing experiments that our parsing algorithm consistently requires less stack depth to recognize annotated trees relative to other algorithms across languages. This result also suggests the existence of a syntactic universal by which deeper center-embedding is a rare construction across languages, a result that has yet to be quantitatively cross-linguistically examined. We further investigate the above claim through supervised parsing experiments and show that our proposed parser is consistently less sensitive to constraints on stack depth bounds when decoding across languages, while the performance of other parsers such as the arc-eager parser is largely affected by such constraints. We thus conclude that the stack depth of our parser represents a more meaningful measure for capturing syntactic regularity in languages than those of existing parsers.
依赖语法的左上角解析
在本文中,我们提出了一种增量依赖解析算法,该算法具有左上角解析策略的弧度渴望变体。我们的算法的堆栈深度捕获识别依赖结构的中心嵌入性。只有在处理较深的中心嵌入句子时,人们才会发现较高的堆栈深度。我们通过对19种语言的树库进行两种实验来检验我们的算法是否能够捕获语言中普遍存在的语法规律性。我们首先通过oracle解析实验证明,相对于其他跨语言的算法,我们的解析算法始终需要更少的堆栈深度来识别带注释的树。这一结果还表明,存在一种句法普遍性,通过这种普遍性,更深层次的中心嵌入是一种罕见的跨语言结构,这一结果尚未得到跨语言的定量检验。我们通过有监督的解析实验进一步研究了上述说法,并表明我们提出的解析器在跨语言解码时对堆栈深度边界的约束始终不太敏感,而其他解析器(如arc-eager解析器)的性能在很大程度上受到这些约束的影响。因此,我们得出结论,与现有的解析器相比,我们的解析器的堆栈深度代表了捕获语言语法规则的更有意义的度量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Information Processing
Journal of Information Processing Computer Science-Computer Science (all)
CiteScore
1.20
自引率
0.00%
发文量
0
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