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Transferring Syntactic Relations from English to Hindi Using Alignments on Local Word Groups

机译:使用当地字组上的对齐转移从英语转移到印地语的句法关系

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Various works have used word alignments in parallel corpora to transfer information like POS tags, syntactic trees and word senses from source to target sentences. In this paper, we work on the problem of projecting syntactic relations from English to morphologically rich Hindi parallel text. We show the effectiveness of Local Word Groups (LWGs) in simplifying alignments as well as in transferring syntactic dependencies by building an alignment model with LWGs as base units and training a dependency parser on the relations projected using these LWGs. The LWG alignment model using GIZA++ scores decreases the Alignment Error Rate by 1.16 points when compared to the best GIZA++ model trained on lemmas. We also show that a dependency parser trained on the syntactic relations projected using LWGs obtained statistical significant improvements over the relations projected using lemmas by a margin of 3.49%.
机译:各种作品在并行语料库中使用了单词对齐来传输像POS标签,句法树和单词感官的信息,从源到目标句子。在本文中,我们致力于将英语句法关系突出到形态学上富有的印地语并行文本的问题。我们通过将LWGS作为基本单位构建一个对齐模型来展示本地字组(LWGS)的有效性以及将句法依赖性构建为基本单位,并在使用这些LWG投影的关系上训练依赖性解析器。与LEMMAS培训的最佳GIZA ++模型相比,使用Giza ++分数的LWG对齐模型将对准错误率降低1.16点。我们还表明,在使用LWG投影的句法关系上培训的依赖解析器获得了使用LEMMAS投影的关系的统计显着改进,其利润率为3.49%。

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