Active learning for concept disambiguation

A computer system for active machine learning, comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:
• annotating a plurality of data entries using rule-based natural language processing;
• parsing the plurality of data entries into a first dataset that includes entities, features, and classifications;
• building, using a bootstrap aggregation, the first dataset based on the annotated plurality of data entries using coreference resolution and entity analysis, wherein each row in the first dataset represents an annotation from the annotated plurality of data entries;
• receiving user feedback based on the built first dataset in response to detecting an ambiguity associated with a data entry in the built first dataset, wherein the ambiguity indicates the data entry comprises more than one meaning;
• assigning a plurality of weights to a plurality of data entry subsets;
• generating a second weighted dataset that is weighted higher than the first dataset because the second weighted dataset is based on the received user feedback;
• and
• transmitting the second weighted dataset to create a trained model.

A computer program product for active machine learning, comprising:

• one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more computer-readable tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:
• annotating a plurality of data entries using rule-based natural language processing;
• parsing the plurality of data entries into a first dataset that includes entities, features, and classifications;
• building, using a bootstrap aggregation, the first dataset based on the annotated plurality of data entries using coreference resolution and entity analysis, wherein each row in the first dataset represents an annotation from the annotated plurality of data entries;
• receiving user feedback based on the built first dataset in response to detecting an ambiguity associated with a data entry in the built first dataset, wherein the ambiguity indicates the data entry comprises more than one meaning;
• assigning a plurality of weights to a plurality of data entry subsets;
• generating a second weighted dataset that is weighted higher than the first dataset because the second weighted dataset is based on the received user feedback;
• and
• transmitting the second weighted dataset to create a trained model.