TDBRNN: An approach to learning users' intention to legal consultation with normalized tensor decomposition and Bi-LSTM

With the development of Internet technology and the enhancement of people's concept of the rule of law, online legal consultation has become an important means for the general public to conduct legal consultation. However, different people have different language expressions and legal professional backgrounds. This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation. How to accurately understand the true intentions behind different users' legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services. Traditional intent understanding algorithms rely heavily on the lexical and semantic information between the original data, and are not scalable, and often require taxing manual annotation work. This article proposes a new approach TdBrnn which is based on the normalized tensor decomposition method and Bi-LSTM to learn users' intention to legal consulting. First, we present the users' legal consulting statements as a tensor. And then we use the normalized tensor decomposition layer proposed by this article to extract the tensor elements and structural information of the original tensor which can best represent users' intention of legal consultation, namely the core tensor. The core tensor relies less on the lexical and semantic information of the original users' legal consulting statements data, it reduces the dimension of the original tensor, and greatly reduces the computational complexity of the subsequent Bi-LSTM algorithm. Furthermore, we use a large number of core tensors obtained by the tensor decomposition layer with users' legal consulting statements tensors as inputs to continuously train Bi-LSTM, and finally derive the users' legal consultation intention classification model which can comprehensively understand the user's legal consultation intention. Experiments show that our method has faster convergence speed and higher accuracy than traditional recurrent neural networks.