6. A computer-implemented method for creating a hierarchical semantic map of a document and extracted information, comprising: apportioning, with a data processor, the document into major sections by accessing the document and recognizing a hierarchical structure of the document; dividing the document into major sections according to the hierarchical structure; accessing a machine learning model including a plurality of classifiers configured to classify portions of the document, each of the plurality of classifiers for a respective level of the hierarchical structure; classifying, with the data processor, each of the major sections of the document by using a first classifier included in the plurality of classifiers, and mapping the major sections to the key elements; searching with the data processor one major section that is mapped to one key element, and identifying sub-sections within the one major section, the sub-sections including granular level pieces of information comprising sub-granular clause types forming the one key element according to the machine learning model; selecting a second classifier from the plurality of classifiers according to features of the one major section, wherein the features achieve a maximum confidence score by the second classifier, wherein the maximum confidence score indicates that the sub-sections associate with at least one of the key elements; classifying, with the processor, each of the granular level pieces of information using the second classifier selected for the one major section, and map each of the sub-sections to at least one of the key elements; extracting the granular level pieces of information corresponding to the sub-granular clause types from the identified sub-sections by using sequence modelers and linguistic characteristics provided by a data profiler, the data profiler configured to recognize linguistics characteristics of the extracted information, wherein the linguistics characteristics comprise predicates, structures, neighboring characters, and types of data that induce regular expressions of the extracted information, wherein the sequence modelers are previously trained based on an annotated corpus; generating the hierarchical semantic map of the document by using the extracted information according to the hierarchical structure, and storing the extracted information and the hierarchical semantic map in a memory storage device database; and presenting in a user interface on a user display device drop down selections of the key elements of the document, and in response to a selection of one of the key elements, displaying the extracted information associated with the selected key element.
6. A computer-implemented method for creating a hierarchical semantic map of a document and extracted information, comprising: apportioning, with a data processor, the document into major sections by accessing the document and recognizing a hierarchical structure of the document; dividing the document into major sections according to the hierarchical structure; accessing a machine learning model including a plurality of classifiers configured to classify portions of the document, each of the plurality of classifiers for a respective level of the hierarchical structure; classifying, with the data processor, each of the major sections of the document by using a first classifier included in the plurality of classifiers, and mapping the major sections to the key elements; searching with the data processor one major section that is mapped to one key element, and identifying sub-sections within the one major section, the sub-sections including granular level pieces of information comprising sub-granular clause types forming the one key element according to the machine learning model; selecting a second classifier from the plurality of classifiers according to features of the one major section, wherein the features achieve a maximum confidence score by the second classifier, wherein the maximum confidence score indicates that the sub-sections associate with at least one of the key elements; classifying, with the processor, each of the granular level pieces of information using the second classifier selected for the one major section, and map each of the sub-sections to at least one of the key elements; extracting the granular level pieces of information corresponding to the sub-granular clause types from the identified sub-sections by using sequence modelers and linguistic characteristics provided by a data profiler, the data profiler configured to recognize linguistics characteristics of the extracted information, wherein the linguistics characteristics comprise predicates, structures, neighboring characters, and types of data that induce regular expressions of the extracted information, wherein the sequence modelers are previously trained based on an annotated corpus; generating the hierarchical semantic map of the document by using the extracted information according to the hierarchical structure, and storing the extracted information and the hierarchical semantic map in a memory storage device database; and presenting in a user interface on a user display device drop down selections of the key elements of the document, and in response to a selection of one of the key elements, displaying the extracted information associated with the selected key element. 10. The computer-implemented method of claim 6 , further comprising: regenerating the machine learning model and the data profiler to be stored in the database according to a feedback for the generated hierarchical semantic map and the extracted information wherein the feedback is received from a subject matter expert through a user interface.