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Professional Jurisdiction Recognition for Cross-Domain Filing Based on Deep Hybrid Model

机译:基于深层混合模型的跨域归档的专业司法识别

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This paper proposes a professional jurisdiction recognition algorithm for case materials in cross-domain filing based on a deep hybrid model. Through the parallel combination of CNN and RNN, the spatial and sequence features of text data can be captured without interfering with each other. In addition, we use the tensor outer product to construct them into a high-order data block with rich information and stronger representation capabilities. Extensive experiments are conducted on a new data set with labeled examples consisting of 2068 case materials from three professional courts and one ordinary courts, and the results demonstrate that the proposed model is effective in professional jurisdiction recognition for cross-domain filing.
机译:本文提出了一种基于深杂布模型的跨域归档案例材料的专业管辖权识别算法。 通过CNN和RNN的并行组合,可以在不干扰对方的情况下捕获文本数据的空间和序列特征。 此外,我们使用张量外产品将它们构建成高阶数据块,具有丰富的信息和更强的表示功能。 广泛的实验是在具有标有2068个职业法院和一个普通法庭的2068个案例材料组成的新数据集的新数据集上,结果表明,该模型对于跨域申请的专业管辖权识别有效。

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