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The application of multi-label text classification in charge prediction aims at forecasting all kinds of charges related to the content of judgment documents according to the actual situation, which plays a vital role in the judgment of criminal cases. Existing classification algorithms have high accuracy for the single-charge prediction, but their accuracy for the multi-charge prediction is low. To solve this problem, in this paper we introduce a novel hierarchical nested attention structure model with relevant law article information to predict the multi-charge classification of legal judgment documents. By considering the correlation between different charges, the accuracy of multi-charge prediction is greatly improved. Experimental results on real-world datasets demonstrate that our proposed model achieves significant and consistent improvements over other state-of-the-art baselines.

Multiview representation has become important due to its good performance for machine learning problems. In this paper, a multiview representation framework based on transfer learning is proposed for micro-expression recognition. The framework takes macro-expression as the auxiliary domain and micro-expression as the target domain, and assists the identification of micro-expressions by transferring the rich information extracted from the auxiliary domain, which effectively addresses the small sample problem of micro-expression recognition. The proposed algorithm mainly consists of three parts. Firstly, the features of the two domains are projected into a common space and the dictionaries of each domain are studied respectively. Then the dictionary of micro-expression domain is linearly reconstructed. Finally, in order to improve the comprehensive utilization of feature information, the most representative features from four different micro-expression feature sets are selected by multiview representation. The experiments and evaluation are carried out on three different databases, and the performance comparison of the proposed algorithm with other advanced methods are given. The experimental results show that the proposed algorithm has the better performance than other related methods.