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Detection of document modification based on deep neural networks

机译:基于深度神经网络的文档修改检测

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In this paper, we focus on the detection of the semantic and structural modifications in documents. We define the following six inter-document relations that we use to represent document modification: Eliminate, Extend, Merge, Split, Rewrite , and Reorder . We also develop a detection model based on a deep neural network to identify the relations between two given documents. We assumed that several modifications can be applied to a document; in this situation, the modifications can overlap each other, so it can be very difficult to detect the applied modifications. We represent a document pair by using a sentence-based similarity matrix, and the inter-document relations are then detected by applying the deep neural network to the similarity matrix. The experiments show that our model performed impressively in the detection of document modifications.
机译:在本文中,我们专注于对文档中语义和结构修改的检测。我们定义了以下六个文档间关系,用于表示文档修改:消除,扩展,合并,拆分,重写和重新排序。我们还开发了一种基于深度神经网络的检测模型,以识别两个给定文档之间的关系。我们假定可以对文档进行多种修改。在这种情况下,修改可能会相互重叠,因此很难检测到所应用的修改。我们使用基于句子的相似度矩阵表示一个文档对,然后通过将深度神经网络应用于相似度矩阵来检测文档间的关系。实验表明,我们的模型在检测文档修改方面表现出色。

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