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Normalizing Complex Functional Expressions in Japanese Predicates: Linguistically-Directed Rule-Based Paraphrasing and Its Application

机译:规范日语谓词中的复杂功能表达式:语言指导的基于规则的措辞及其应用

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摘要

The growing need for text mining systems, such as opinion mining, requires a deep semantic understanding of the target language. In order to accomplish this, extracting the semantic information of functional expressions plays a crucial role, because functional expressions such as would like to and can't are key expressions to detecting customers' needs and wants. However, in Japanese, functional expressions appear in the form of suffixes, and two different types of functional expressions are merged into one predicate: one influences the factual meaning of the predicate while the other is merely used for discourse purposes. This triggers an increase in surface forms, which hinders information extraction systems. In this article, we present a novel normalization technique that paraphrases complex functional expressions into simplified forms that retain only the crucial meaning of the predicate. We construct paraphrasing rules based on linguistic theories in syntax and semantics. The results of experiments indicate that our system achieves a high accuracy of 79.7%, while it reduces the differences in functional expressions by up to 66.7%. The results also show an improvement in the performance of predicate extraction, providing encouraging evidence of the usability of paraphrasing as a means of normalizing different language expressions.
机译:对文本挖掘系统(例如意见挖掘)的需求不断增长,需要对目标语言有深刻的语义理解。为此,提取功能表达式的语义信息起着至关重要的作用,因为诸如愿意和不希望这样的功能表达式是检测客户需求的关键表达式。但是,在日语中,功能性表达式以后缀形式出现,并且两种不同类型的功能性表达式合并为一个谓词:一种影响谓词的事实含义,而另一种仅用于论述目的。这触发了表面形式的增加,这阻碍了信息提取系统。在本文中,我们提出了一种新颖的归一化技术,该技术将复杂的功能表达式释义为简化形式,仅保留谓词的关键含义。我们基于语法和语义上的语言理论构造释义规则。实验结果表明,我们的系统达到了79.7%的高精度,同时减少了66.7%的功能性表达差异。结果还显示谓词提取的性能有所提高,提供了令人鼓舞的证据,表明释义可作为一种标准化不同语言表达的方式。

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