1. One or more computer-readable media having embodied thereon computer-usable instructions that, when executed, facilitate a method of segment extraction by a user for a machine learning system, the method comprising: storing a set of data items, wherein each data item includes a plurality of tokens; providing a segment extractor that is trainable to identify a segment within a data item as an example of a concept, wherein the segment includes a group of tokens; presenting on a user interface a concept hierarchy that represents the concept, wherein the concept hierarchy includes hierarchical sub-concepts that are constituent parts of the concept, and wherein one or more of the sub-concepts are user-selectable for labeling tokens within the data item as examples of a selected sub-concept; receiving a user selection of a sub-concept in the concept hierarchy; utilizing the segment extractor to select from the plurality of data items a first data item that is predicted to include an example of the concept associated with the concept hierarchy, wherein the example is represented by one or more of the tokens in the first data item; displaying the first data item, wherein displaying the first data item includes presenting a first set of one or more pre-labels that identify a first set of one or more tokens as predicted positive examples of the selected sub-concept; receiving a user selection of a first token in the displayed second data item that labels the first token as a positive or negative example of the selected sub-concept; replacing the first set of one or more pre-labels with a second set of one or more pre-labels that identify a second set of one or more tokens as predicted positive examples of the selected sub-concept; and based at least on the labeling of the first token as an example of the selected sub-concept, training the segment extractor.