Case2vec: joint variational autoencoder for case text embedding representation

Ran Song, Shengxiang Gao, Zhengtao Yu, Yafei Zhang, Gaofeng Zhou

Abstract: The embedding representation of the case text represent text as vector which consist information of original texts abundantly. Text embedding representation usually uses text statistical features or content features alone. However, case texts have characteristics that include similar structure, repeated words, and different text lengths. And the statistical feature or content feature cannot represent case text efficiently. In this paper, we propose a joint variational autoencoder (VAE) to represent case text embedding representation. We consider the statistical features and content features of case texts together, and use VAE to align the two features into the same space. We compare our representations with existing methods in terms of quality, relationship, and efficiency. The experiment results show that our method has achieved good results, which have higher performance than the model using single feature.