Abstract: From metadata corresponding to a narrative text, a first encoding is constructed, the first encoding comprising a standardized text string, the first encoding formed according to an encoding scheme. A specified portion of the standardized text string of the first encoding is marked as an anchor term. A correspondence between the first encoding and a second encoding is tested using the encoding scheme and a Natural Language Processing engine, responsive to finding the anchor term within the narrative text. The second encoding corresponds to a text window. The text window comprises a portion of the narrative text comprising an instance of the anchor term and a word within a predetermined distance from the instance. Responsive to the second encoding being identical to the first encoding, the narrative text is annotated, the annotating creating new data linking the narrative text with the second encoding.

Abstract: From metadata corresponding to a narrative text, a first encoding is constructed, the first encoding comprising a standardized text string, the first encoding formed according to an encoding scheme. A specified portion of the standardized text string of the first encoding is marked as an anchor term. A correspondence between the first encoding and a second encoding is tested using the encoding scheme and a Natural Language Processing engine, responsive to finding the anchor term within the narrative text. The second encoding corresponds to a text window. The text window comprises a portion of the narrative text comprising an instance of the anchor term and a word within a predetermined distance from the instance. Responsive to the second encoding being identical to the first encoding, the narrative text is annotated, the annotating creating new data linking the narrative text with the second encoding.

Abstract: Performing an operation comprising extracting, from an input comprising unstructured electronic text, a plurality of feature values for a plurality of features defined in a feature vector, identifying, based on a machine learning (ML) model applied to the plurality of feature values, a portion of the electronic text corresponding to an adverse event, and annotating the portion of the electronic text with an indication of the identified adverse event.

Abstract: Performing an operation comprising extracting, from an input comprising unstructured electronic text, a plurality of feature values for a plurality of features defined in a feature vector, identifying, based on a machine learning (ML) model applied to the plurality of feature values, a portion of the electronic text corresponding to an adverse event, and annotating the portion of the electronic text with an indication of the identified adverse event.