Judgments recommendation method based on multi-modal feature fusion

In order to address the problems such as "different judgments in similar cases" caused by the traditional way of handling cases, and to satisfy the parties' needs to search and preview the judgments which are similar to their own situation, this paper proposes a judgments recommendation method based on multi-modal feature fusion, which can learn high-level fusion feature representation from multiple modalities for the judgments. Then the judgments recommendation can be performed using the learned fusion feature. The proposed method consists of four stages, i.e., data preprocessing, feature extraction, mult-modal feature fusion, and judgments recommendation. Extensive experiments demonstrate that, compared to the methods using only single-modal features and simple concatenation of multi-modal features, the multi-modal fusion features learned by our method achieves significant improvement in precision, recall, and F1 Value for judgments recommendation. It shows that the multi-modal feature fusion method proposed in this paper is effective for judgments recommendation.