Advanced Neural Model for Spanish Spell-Checking

Context: Correcting spelling errors in written content, particularly in Spanish texts, remains a critical challenge in natural language processing (NLP) due to the complexity of word structures and the inefficiency of existing methods when applied to large datasets. Method: This paper introduces a novel neural model inspired by the brain’s cognitive mechanisms for recognizing and correcting misspelled words. Through a deep hierarchical framework with specialized recognition neurons and advanced activation functions, the model is designed to enhance the accuracy and scalability of spelling correction systems. Our approach not only improves error detection but also provides context-aware corrections. Results: The results show that the model achieves an F-measure of 83%, significantly surpassing the 73% accuracy of traditional spell-checkers, marking a substantial advancement in automated spelling correction for the Spanish language. Conclusions: The features of the neural model facilitate spelling correction by emulating the cognitive mechanisms of the human mind. Our model detects more orthographic error types and reports less false positives. As for its limitations, this proposal requires the supervised definition of the weights assigned to the variables used for recognition.