Abstract
On R&D projects, automated analysis of implicational and morphological changes between two documents helps managers and researchers to understand projects. However, it is not easy to manually analyze changes between two documents. In this paper, we define text operations which represent changes of texts and make multi-labeled dataset by applying several text operations. Lastly, we propose a method to detect changes of contents. Proposed method represents two documents into an S-matrix first. Next, we use S-matrix as input of Deep Convolutional Neural Networks to identify text operations on the multi-labeled dataset. Experimental results show the effectiveness of our proposed method.
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Acknowledgements
This research was supported by 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). And This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (B0101-16-0559)
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Kim, Nr., Choi, Y., Lee, H., Lee, JH. (2017). Detection of Content Changes Based on Deep Neural Networks. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_124
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DOI: https://doi.org/10.1007/978-981-10-3023-9_124
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