Abstract
Deep Recurrent Neural Network has an excellent performance in sentence semantic analysis. However, due to the curse of the computational dimensionality, the application in the long text is minimal. Therefore, we propose a Triplet Embedding Convolutional Recurrent Neural Network for long text analysis. Firstly, a triplet from each sentence of the long text. Then the most crucial head entity into the CRNN network, composed of CNN and Bi-GRU networks. Both relation and tail entities are input to a CNN network through three splicing layers. Finally, the output results into the global pooling layer to get the final results. Entity fusion and entity replacement are also used to retain the text’s structural and semantic information before triplet extraction in sentences. We have conducted experiments on a large-scale criminal case dataset. The results show our model significantly improves the judgment prediction task.
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Acknowledgements
This work was supported by the “Six talent peaks" High Level Talents of Jiangsu Province (XYDXX-204), Province Key R &D Program of Jiangsu (BE2020026), XJTLU Research Development Funding (RDF-20-02-10), Suzhou Science and Technology Development Planning Programme-Key Industrial Technology Innovation-Prospective Applied Basic Research Project (SGC2021086), Special Patent Research Project of China National Intellectual Property Office (Y220702).
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Liu, J., Zhu, M., Ouyang, H., Sun, G., Li, H. (2022). Triplet Embedding Convolutional Recurrent Neural Network for Long Text Semantic Analysis. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_43
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