부산대학교 도서관

Drug abuse has always been a severe issue, but the proportion of drug abuse and addiction is rising. According to research reports, youth are motivated to access drugs mainly due to curiosity and peer influence. Additionally, youth especially lack proper knowledge and education surrounding drug abuse. Analyzing whether potential addicts intend to access drugs is helpful in preventing drug abuse and addiction. We developed an Anti-drug Chatbot for young people on a popular online social platform. We can detect potential risks, obtain warnings from the user-entered query and provide these to professional consultants for help. In this article, we present a hierarchical system with bidirectional encoder representation from transformers (BERT) to efficiently recognize and classify a user’s intent. We use the Chinese BERT-based model to utilize contextual information to perform classification and recognition. We evaluate our proposed system on our conversational dataset.