Learning Users’ Intention of Legal Consultation through Pattern-Oriented Tensor Decomposition with Bi-LSTM

Abstract

Online legal consultation plays an increasingly important role in the modern rule-of-law society. This study aims to understand the intention of legal consultation of users with different language expressions and legal knowledge background. A critical issue is a method through which users’ legal consultation data are classified and the feature of each category is extracted. Traditional classification methods rely considerably on lexical and syntactic features and frequently require strict sentence formatting, which eliminates substantial energy and may not be universally applicable. We aim to extract the patterns of users’ consultation on different categories, which minimally depend on lexical, syntax, and sentence formatting. However, research in this area has rarely been conducted in previous legal advisory service studies. In this study, a classification approach for multiclass users’ intention based on pattern-oriented tensor decomposition and Bi-LSTM is proposed, and each user’s legal consulting statement is expressed as a tensor. Moreover, we propose a pattern-oriented tensor decomposition method that can obtain a core tensor that approximates the patterns of users’ consultation. These patterns can improve the accuracy of classifying users’ intention of legal consultation. We use Bi-LSTM to automatically learn and optimize these patterns. Evidently, Bi-LSTM with a pattern-oriented tensor decomposition layer performs better than a recurrent neural network only. Results show that our method is more accurate than the previous work, and the factor matrix and core tensor calculated by the pattern-oriented tensor decomposition are interpretable.