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Incorporating Prepositional Phrase Classification Knowledge in Prepositional Phrase Identification

机译:将介词短语分类知识整合到介词短语识别中

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This paper proposes a method of prepositional phrase (PP) identification by incorporating PP classification knowledge. When PPs act as different syntactic constituents, they have different characteristics in terms of location and context. In this paper, PPs are classified based on the context in which they appear. We select features based on the category of PPs to train multiple machine learning models for PP identification, and recombine these identification results. In this way, we can make full use of the complementary advantage of multiple models.
机译:本文提出了一种通过结合PP分类知识来识别介词短语(PP)的方法。当PP充当不同的语法成分时,它们在位置和上下文方面具有不同的特征。在本文中,PP是根据出现的上下文进行分类的。我们根据PP的类别选择功能,以训练用于PP识别的多种机器学习模型,并重新组合这些识别结果。这样,我们可以充分利用多种模型的互补优势。

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