CRYSTAL CLARIFY – IDENTIFYING PURPOSE WITH INTENT CLASSIFICATION
M. Senthilpandian1, K. Nanditha2, M. Keerti2, Saumya Singh2, S. Deepa Nivethika2
1School of Civil Engineering, 2School of Computer Science and Engineering
Vellore Institute of Technology, Chennai Campus, India
ABSTRACT: The development of artificial intelligence technologies has significantly influenced the field of natural language processing, particularly in the context of conversational interfaces such as chatbots and virtual assistants. Intent classification, a crucial component of NLP, plays a pivotal role in enabling machines to understand and interpret human language effectively. This paper delves into implementing a Convolutional Neural Network (CNN)-based intent classification system for chatbots, focusing on achieving high accuracy and robustness across various intents and user scenarios. The core objective of intent classification is to decipher the underlying meaning or intention behind user inputs, facilitating the delivery of relevant and contextually appropriate responses. In the contemporary digital landscape, where conversational interfaces have become ubiquitous, accurate intent classification is essential for enhancing user experiences and interactions with AI-driven systems. Our approach leverages the capabilities of CNNs, which are known for their efficiency in tasks such as image recognition and text analysis. Specifically tailored for intent classification, CNNs offer advantages such as efficient feature extraction, scalability for handling large datasets, and the ability to capture complex patterns in textual data. These attributes make CNNs well-suited for the nuanced task of understanding user intents from diverse language inputs. The significance of this project lies in its contribution to advancing interactive technologies and human-machine interaction. We aim to improve chatbot systems’ usability, responsiveness, and intelligence by developing a reliable intent classification model. This aligns with the broader goal of creating more intuitive and seamless digital experiences for users across different domains and applications. In summary, this paper comprehensively explores CNN-based intent classification for chatbots, highlighting its relevance in the realm of NLP and conversational interfaces. Through rigorous experimentation and validation, we aim to showcase the effectiveness of our system in accurately categorizing user intents, thereby paving the way for enhanced AI-driven interactions.
KEYWORDS: Intent Classification, Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Training Pipeline, Human-Machine Interaction