Abstract
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.
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Acknowledgements
This research was supported by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (NRF-2014M3C4A7030503). This research was also supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research & Development Program 2016 (R2016030046).
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Kim, Nr., Choi, Y., Lee, H. et al. Detection of document modification based on deep neural networks. J Ambient Intell Human Comput 9, 1089–1096 (2018). https://doi.org/10.1007/s12652-017-0617-y
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DOI: https://doi.org/10.1007/s12652-017-0617-y