Examples of automatically generating natural language pipelines to process an input to generate tags, semantic or syntactic, are described. In an example, on receiving a request to process input data to generate tags, a dependency graph, based on identified dependees and further dependees may be created to satisfy the request, the dependency graph including natural language operations arranged in order of their dependencies on each other. Based on the dependency graph, a pipeline for the tags may be automatically generated, which includes a series of natural language operations such that the operations for dependee tags are processed before any of their associated depender tags. Further, the dependency graph and the automated pipeline generation allows for automated optimization of the pipeline, training, re-training, testing and regression testing of the semantic tags and supporting machine learning models and provides a framework to efficiently manage the sharing and reuse of semantic understanding operations.