Unveiling the Potential of LSTM-based Chatbots with Embeddings for University Communication
Keywords:
Adam Optimizer, Chatbot, Context-aware, Conversational AI, Feedback, Keras, LSTM, Multi-class Classification, Natural Language Processing, Prediction, Response Generation, Sequential Model, User Interface.Abstract
In the ever-evolving landscape of educational technology, the integration of has garnered considerable attention for its potential support in academic institutions. This paper presents a comprehensive exploration into the development and implementation of an advanced chatbot system tailored specifically for university environments. Our approach leverages the augmented with word embeddings to construct a robust and context-aware conversational model. By embedding words into a continuous vector space, we understand user queries and responses. The LSTM architecture furthers the, making it particularly suitable for processing natural language conversations. We describe the design and training process of the LSTM-based chatbot, focusing on key components such as tokenization, sequence padding, and label encoding. Moreover, we elucidate the model's architecture, encompassing embedding layers, LSTM cells, and dense layers, optimized for multi-class classification tasks. Through extensive experimentation and training on a diverse dataset sourced from university-related intents, the chatbot achieves high accuracy and fluency in generating contextually relevant responses. Furthermore, we investigate the deployment and integration of the chatbot within university. User interaction with the chatbot is facilitated through a user-friendly interface, allowing seamless access to academic information, student services, and administrative support. Additionally, we discuss the iterative refinement process, incorporating user feedback and system analytics to continuously improve the chatbot's performance and user experience.
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