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US 10,540,438 B2Granted
Cognitive framework to detect adverse events in free-form text
AssigneeInternational Business Machines Corporation
InventorsMeenakshi Nagarajan, Cartic Ramakrishnan, Sheng Hua Bao, Nakul Chakrapani
Life of this patent
Estimating the term…
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.
Claims20
A computer-implemented method, comprising:
- extracting a plurality of grammatical features of a corpus of electronic text data;
- generating, based at least in part on the plurality of grammatical features, a plurality of context embedding vectors;
- defining a plurality of features in a feature vector based on the plurality of context embedding vectors, comprising:
- clustering the plurality of context embedding vectors, wherein a first feature of the plurality of features is defined based on a dependency between a first term and a second term in the corpus of electronic text data;
- determining a first cluster identifier of a first cluster associated with the first term;
- determining a second cluster identifier of a second cluster associated with the second term;
- and
- modifying the first feature to specify a dependency between the first and second cluster identifiers;
- extracting, from an input comprising unstructured electronic text, a plurality of feature values for the plurality of features defined in the 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, by a computer processor, the identified portion of the electronic text with an indication of the adverse event.
9 more elements
A system, comprising:
- a processor;
- and
- a memory containing a program which when executed by the processor performs an operation comprising:
- extracting a plurality of grammatical features of a corpus of electronic text data;
- generating, based at least in part on the plurality of grammatical features, a plurality of context embedding vectors;
- defining a plurality of features in a feature vector based on the plurality of context embedding vectors, comprising:
- clustering the plurality of context embedding vectors, wherein a first feature of the plurality of features is defined based on a dependency between a first term and a second term in the corpus of electronic text data:
- determining a first cluster identifier of a first cluster associated with the first term;
- determining a second cluster identifier of a second cluster associated with the second term;
- and
- modifying the first feature to specify a dependency between the first and second cluster identifiers;
- extracting, from an input comprising unstructured electronic text, a plurality of feature values for the plurality of features defined in the 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 identified portion of the electronic text with an indication of the adverse event.
12 more elements
A computer program product, comprising:
- a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by a processor to perform an operation comprising:
- extracting a plurality of grammatical features of a corpus of electronic text data;
- generating, based at least in part on the plurality of grammatical features, a plurality of context embedding vectors;
- defining a plurality of features in a feature vector based on the plurality of context embedding vectors, comprising:
- clustering the plurality of context embedding vectors, wherein a first feature of the plurality of features is defined based on a dependency between a first term and a second term in the corpus of electronic text data:
- determining a first cluster identifier of a first cluster associated with the first term;
- determining a second cluster identifier of a second cluster associated with the second term;
- and
- modifying the first feature to specify a dependency between the first and second cluster identifiers;
- extracting, from an input comprising unstructured electronic text, a plurality of feature values for the plurality of features defined in the 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, by a computer processor, the identified portion of the electronic text with an indication of the adverse event.
10 more elements
Dependencies are inferred from the claim text.