Traditional intent classifiers map utterances directly to intent categories using labeled examples. In contrast, LLM-assisted zero-shot intent classifiers rely on natural language descriptions alongside intent labels to determine the most appropriate categories for a given utterance. As intent taxonomies grow and evolve, descriptions must define clear boundaries among overlapping intent categories. Maintaining them is costly because it requires domain expertise and continual updates to keep pace with changing product and user behavior. We present MOSAIC (Multi-label Ontology Synthesis for Automated Intent Classification), a deployed system that automatically generates and updates intent descriptions for an existing taxonomy. MOSAIC analyzes unlabeled user utterances to identify coherent clusters. It associates cluster-level evidence with the existing intent taxonomy. Then, it synthesizes discriminative descriptions that define each category’s semantic boundary. On a 140-class production deployment, MOSAIC approaches the quality of expert-written descriptions while reducing taxonomy revision time from 2-4 weeks to less than 30 minutes. On the public NLU++ and Multi³NLU++ benchmark datasets, MOSAIC-generated descriptions do match or exceed the performance of expert-written descriptions.

Conversational systems are of primary interest in the AI community. Organizations are increasingly using chatbot to provide round-the-clock support and to increase customer engagement. Many commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize user input. These frameworks require a collection of user utterances and corresponding intent to train an intent model. Collecting a substantial coverage of training data is a bottleneck in the bot building process. In cases where past conversation data is available, the cost of labeling hundreds of utterances with intent labels is time-consuming and laborious. In this paper, we present an intent discovery framework that can mine a vast amount of conversational logs and to generate labeled data sets for training intent models. We have introduced an extension to the DBSCAN algorithm and presented a density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Empirical evaluation on one conversation dataset, six different intent dataset, and one short text clustering dataset show the effectiveness of our hypothesis.