15. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: ingesting training data representative of documents to be analyzed by a natural language model, wherein the training data includes at least one of a first document and a portion of the first document; generating a hierarchical data structure, the hierarchical data structure comprising at least two topical nodes, wherein the at least two topical nodes represent partitions organized by two or more topical themes among the topical content of the training data within which the training data is to be subdivided into; selecting among the training data a plurality of documents to be annotated; determining, for each document among the plurality of documents, a level of ambiguity in interpreting said document that the natural language platform is trying to resolve, wherein the level of ambiguity is dependent upon information currently possessed by the natural language platform; generating an annotation prompt for each document among the plurality of documents to be annotated, said annotation prompt being dynamically generated as either a first level prompt corresponding to a first level of specificity or a second level prompt corresponding to a second level of specificity, wherein both the first level prompt and the second level prompt comprise a human readable textual instruction worded according to the first level of specificity or the second level of specificity, and the first level prompt and the second level prompt are presented alternatively, the first level of specificity and the second level of specificity corresponding to the level of ambiguity of said document, said annotation prompt configured to elicit an annotation about said document designed to resolve said level of ambiguity and indicating which node among the at least two topical nodes of the hierarchical data structure said document is to be classified into, wherein the first level of specificity comprises a first level of true-or-false question and the second level of specificity comprises a multiple-choice question comprising at least three options, wherein the first level of specificity corresponds to a lower level of ambiguity than the second level of specificity; causing display of the at least one annotation prompt for each document among the plurality of documents to be annotated; receiving for each document among the plurality of documents to be annotated, the annotation in response to the displayed annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine, among the received annotations, patterns for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.
15. A non-transitory computer readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: ingesting training data representative of documents to be analyzed by a natural language model, wherein the training data includes at least one of a first document and a portion of the first document; generating a hierarchical data structure, the hierarchical data structure comprising at least two topical nodes, wherein the at least two topical nodes represent partitions organized by two or more topical themes among the topical content of the training data within which the training data is to be subdivided into; selecting among the training data a plurality of documents to be annotated; determining, for each document among the plurality of documents, a level of ambiguity in interpreting said document that the natural language platform is trying to resolve, wherein the level of ambiguity is dependent upon information currently possessed by the natural language platform; generating an annotation prompt for each document among the plurality of documents to be annotated, said annotation prompt being dynamically generated as either a first level prompt corresponding to a first level of specificity or a second level prompt corresponding to a second level of specificity, wherein both the first level prompt and the second level prompt comprise a human readable textual instruction worded according to the first level of specificity or the second level of specificity, and the first level prompt and the second level prompt are presented alternatively, the first level of specificity and the second level of specificity corresponding to the level of ambiguity of said document, said annotation prompt configured to elicit an annotation about said document designed to resolve said level of ambiguity and indicating which node among the at least two topical nodes of the hierarchical data structure said document is to be classified into, wherein the first level of specificity comprises a first level of true-or-false question and the second level of specificity comprises a multiple-choice question comprising at least three options, wherein the first level of specificity corresponds to a lower level of ambiguity than the second level of specificity; causing display of the at least one annotation prompt for each document among the plurality of documents to be annotated; receiving for each document among the plurality of documents to be annotated, the annotation in response to the displayed annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine, among the received annotations, patterns for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure. 16. The computer readable medium of claim 15 , wherein the operations further comprise: testing performance of the natural language model using a subset of the documents among the training data that received annotations.