What ML technique accelerated the formalization of intent recognition as a distinct NLP problem?

Supervised classification

The progression of intent recognition into a formally recognized and solvable challenge within Natural Language Processing (NLP) was directly tied to the increasing power of specific machine learning approaches. Supervised classification techniques were crucial because they allowed researchers to train models effectively using labeled datasets, where known user utterances were explicitly paired with their correct intent labels. Once techniques like Support Vector Machines (SVMs) proved capable of handling the inherent variance found in human language through this classification framework, the field solidified. This adoption meant that intent recognition could be treated as a standard text classification task, moving beyond the limitations of earlier, brittle rule-based systems.