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Journal of Artificial Intelligence and Robotics


Neurosymbolic AI (NeSy AI) represents a groundbreaking approach in the realm of Natural Language Processing (NLP), merging the pattern recognition of neural networks with the structured reasoning of symbolic AI to address the complexities of human language. This study investigates the effectiveness of neurosymbolic AI in providing nuanced understanding and contextually relevant responses, driven by the need to overcome the limitations of existing models in handling complex linguistic tasks and abstract reasoning. Employing a hybrid methodology that combines multimodal contextual modeling with rule-governed inferences and memory activations, the research delves into specific applications like Named Entity Recognition (NER), where architectures such as BiLSTM + CRF demonstrate improved accuracy by analyzing entire sentence contexts. The results affirm the potential of neurosymbolic AI in enhancing linguistic resolutions, semantic ambiguity resolution, and overall language understanding capabilities. Notably, the study showcases the significant strides in improving NER tasks, highlighting this approach’s practical implications and effectiveness. The evolution of neurosymbolic AI, as indicated by this research, exemplifies the ongoing pursuit to create more sophisticated, accurate, and human-like interactions between machines and human language, promising a transformative impact on various sectors, including healthcare and education. The findings pave the way for future research and development in AI, pushing the boundaries of the role of technology in understanding and interacting with human language.

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