Intent Detection on Indonesian Text Using Convolutional Neural Network

Abstract

NLP (Natural Language Processing) has become the focus of research in recent years. NLP tasks have been implemented in various sectors and fields. The chatbot system is one of the NLP tasks, which functions to communicate with humans using natural language. Many researchers build models to represent the chatbot. To make a chatbot more powerful, the intent of the conversation a set of sentences representing a specific user's intention when interacting with the chatbot, must be classified. This classification will make the chatbot system more focused, which leads to providing appropriate answers. Humans can simply understand the meaning of different sentences with the same intent. However, a chatbot system will require a complex technique. Therefore, our work uses the CNN (Convolutional Neural Network) for intent detection in Indonesian Language Text using ATIS (Airline Travel Information System) dataset. CNN was selected because it can extract important features from input data, which makes it more efficient than other deep learning algorithms, in terms of memory and complexity. In our work, we also used GloVe (Global Vectors) embedding for generating an optimal intent classification model. The result shows that the GloVe model and CNN produce the best accuracy of 95.84%.