Automatic Recognition of Parallel Sentence Based on Sentences-Interaction CNN and Its Application

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI:10.1109/icccs55155.2022.9846217

Guanghui Liu, Lijun Fu, Boyuan Yu, Ligong Cui

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Abstract

Automated essay evaluation is to make the computer simulating human evaluate students’ essay. The final grade of one essay is highly related to its literacy. The use of rhetorical devices can increase the literacy of one essay, and parallelism is one of the common rhetoric. Therefore, the identification of parallelism can contribute to automated essay evaluation. However, the research of automatic recognition of parallel sentence is so rare so far. In this paper, we design a deep learning method based on sentences interactive-convolutional neural network to recognize parallelism in the light of word co-occurrence, similar syntactic structure and tone of expression. Firstly, one or more interactive matrices whose elements represent the similarities in some ways between every token in two sentences are constructed. Then the convolutional neural network is employed to capture local features. The experimental results show that our feature selection is effective and the recognition effect is comparable with similar studies.

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基于句子交互CNN的平行句自动识别及其应用

论文自动评阅是指用计算机模拟人对学生的论文进行评阅。一篇文章的最终成绩与它的读写能力密切相关。修辞手法的运用可以提高文章的识字率，并列是常用的修辞手法之一。因此，平行性的识别可以有助于自动论文评估。然而，到目前为止，对平行句自动识别的研究还很少。在本文中，我们设计了一种基于句子交互卷积神经网络的深度学习方法，从词共现、相似句法结构和表达语气三个方面来识别并行性。首先，构建一个或多个交互矩阵，其元素表示两个句子中每个标记之间在某些方面的相似性。然后利用卷积神经网络捕获局部特征。实验结果表明，我们的特征选择是有效的，识别效果与同类研究相当。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 7th International Conference on Computer and Communication Systems (ICCCS)

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