论文标题
使用RASA在封闭的域上构建聊天机器人
Building a Chatbot on a Closed Domain using RASA
论文作者
论文摘要
在这项研究中，我们使用诸如SVM之类的诸如SVM诸如svm，用于提取实体的CRF和LSTM进行分类的几个模型，在封闭的域中构建了一个聊天机器人系统，以预测动作。为了改善机器人的响应，使用KNN算法将提取的虚假实体转化为真实实体。我们聊天机器人的知识领域是关于越南康托大学的信息与通信技术学院。我们手动构建了一个聊天机器人语料库，具有19个意图，441个意图的句子模式，253个实体和133个故事。实验结果表明，机器人对相关问题的反应很好。
In this study, we build a chatbot system in a closed domain with the RASA framework, using several models such as SVM for classifying intents, CRF for extracting entities and LSTM for predicting action. To improve responses from the bot, the kNN algorithm is used to transform false entities extracted into true entities. The knowledge domain of our chatbot is about the College of Information and Communication Technology of Can Tho University, Vietnam. We manually construct a chatbot corpus with 19 intents, 441 sentence patterns of intents, 253 entities and 133 stories. Experiment results show that the bot responds well to relevant questions.
