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Ontology of Academic Sentence Dependencies for a Verb Choice Suggestion

机译:动词选择建议的学术句子依赖性的本体论

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Non-native researchers encountered a problem of lacking academic writing skill. During the writing, we may accidentally use a word repeatedly due to our familiarity that reduces a quality of writing. To solve the problem, a paraphrase is a good option. It helps the manuscript read more flowery by reducing duplicate words and refining sentence alignments. In this study, we propose a novel idea to use a sentence dependency ontology to suggest possible verbs replaceable on existing context without influence to the original intention. We created an ontology-based system and designed a new ontology. To discover a list of verb choices, our idea is based on sentence dependency, especially a dependency between subject and verb (nsubj) as well as a relationship between verb and object (dobj). We chose them because these two dependencies had a strong relationship to the verb of the sentence. To evaluate the system, we compared the efficiencies of two different systems, i.e., a tradition system utilizing synonyms as word choices and our ontology-based system. As the results, ours provided the better performance rather than the traditional system. This clarifies that our system should be a proper solution for studies on paraphrasing.
机译:非原生研究人员遇到了缺乏学术写作技能的问题。在写作期间,由于我们的熟悉程度降低了写作质量,我们可能会不小心使用一个单词。为了解决这个问题,释义是一个很好的选择。它通过减少重复单词和精炼句子对齐来帮助稿件读取更多流动。在这项研究中,我们提出了一种新颖的想法来使用句子依赖性本体学,建议在现有背景下更换可能的动词而不会影响原始意图。我们创建了一个基于本体的系统,并设计了一个新的本体论。要发现动词选择列表,我们的想法基于句子依赖项,尤其是主题和动词(NSUBJ)之间的依赖关系以及动词和对象之间的关系(DOBJ)。我们选择了它们,因为这两个依赖关系与句子的动词有很强的关系。为了评估系统,我们比较了两个不同系统的效率,即,传统系统利用单词选择和基于Ontology的系统。结果,我们提供了更好的性能而不是传统系统。这阐明了我们的系统应该是对释义研究的适当解决方案。

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