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Converting Dependency Structure Into Persian Phrase Structure

机译:将依存结构转换为波斯语短语结构

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摘要

Treebank is one of the important and useful resources in natural language processing represented in two different annotated schemas: phrase and dependency structures. There are many works that convert a phrase structure into a dependency structure and vice versa. Most of them are based that exploit the handcrafted head percolation table and argument table in predefined deterministic ways. In this article, we propose a method to convert a dependency structure into a phrase structure by enriching a trainable model of former hybrid strategy approach. By adding a classifier to the algorithm and using postprocessing modification, the quality of conversion is increased. We evaluate our method in two different languages, English and Persian, and then analyze the errors. The results of our experiments show a 46.01% reduction of error rate in English and 76.50% for Persian compared to our baseline. We build a new phrase structure treebank by converting 10,000 sentences of Persian dependency treebank into corresponding phrase structures and correcting them manually.
机译:树库是自然语言处理中重要且有用的资源之一,以两种不同的带注释模式表示:短语和依赖结构。有许多作品将短语结构转换为依赖结构,反之亦然。它们中的大多数都是以预定的确定性方式利用手工制作的头部渗透表和参数表的基础。在本文中,我们提出了一种通过丰富以前的混合策略方法的可训练模型将依赖关系结构转换为短语结构的方法。通过将分类器添加到算法并使用后处理修改,可以提高转换质量。我们用两种不同的语言(英语和波斯语)评估我们的方法,然后分析错误。我们的实验结果表明,与基线相比,英语的错误率降低了46.01%,波斯语的错误率降低了76.50%。通过将10,000个波斯依赖树库的句子转换为相应的短语结构并手动更正,我们构建了一个新的短语结构树库。

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