An Evolutive Knowledge Base for “AskBot” Toward Inclusive and Smart Learning-based NLP Techniques

Artificial Intelligence chatbots have shown a growing interest in different domains including e-learning. They support learners by answering their repetitive and massive questions. In this paper, we develop a smart learning architecture for an inclusive chatbot handling both text and voice messages. Thus, disabled learners can easily use it. We automatically extract, preprocess, vectorize, and construct AskBot's Knowledge Base. The present work evaluates various vectorization techniques with similarity measures to answer learners' questions. The proposed architecture handles both Wh-Questions starting with Wh words and Non-Wh-Questions, beginning with unpredictable words. Regarding Wh-Questions, we develop a neural network model to classify intents. Our results show that the model's accuracy and the F1-Score are equal to 99,5%, and 97% respectively. With a similarity score of 0.6, our findings indicate that TF-IDF has performed well, correctly answering 90% of the tested Wh-Questions. Concerning No-Wh Questions, soft cosine measure, and fasttext successfully answered 72% of Non-Wh-Question.

@article{Azhari2023,
  title     = {An Evolutive Knowledge Base for “AskBot” Toward Inclusive and Smart Learning-based NLP Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {5},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Khadija El Azhari and Imane Hilal and Najima Daoudi and Rachida Ajhoun and Ikram Belgas},
  doi       = {10.14569/IJACSA.2023.0140544},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140544}
}