A study of deep semantic matching in question-and-answer events in civil litigation in the environmental justice system

Abstract

Abstract Information retrieval and text mining fields extensively utilize text semantic matching models. In this paper, civil litigation Q&A under the environmental justice system is taken as a specific research field, and after constructing a civil litigation Q&A system based on deep learning, two of the key techniques—question categorization and semantic matching—are selected as the main research content. Specifically, the ALBERT algorithm is used to extract word vectors, and the hidden feature vectors are obtained through BiLSTM modeling of contextual relationships and then combined with the Attention mechanism for scoring and weighting to obtain the final text-level vectors for classification so as to establish the civil litigation question classification model based on ALBERT. Then, we establish the BERT-based civil litigation question and answer matching model by sorting the set of candidate answers by semantic matching degree based on the BERT algorithm. Selected datasets and comparison algorithms are experimented with, and the analysis shows that the question classification model has a better effect than civil litigation question text classification, and the values of each index have been improved by 0.75%~3.00% on the basis of the baseline model. The MAP and MRR values (0.76~0.86) of the question-matching model are higher than those of the comparison model, verifying its superior performance in semantically assigning characters. The model proposed in this paper is more useful because it can provide civil litigation counseling to the public.