Method for transforming data elements within a classification system based in part on input from a human annotator or expert
First Claim
1. A method for evolving an annotating model for classifying a document or a data item therein, comprising:
- composing a first concept evolution model as a training set comprised of a first set of selectively determinable class labels of element instances within the document that are detectable within the document to produce a result of predicting class labels to be assigned to unlabeled element instances and the first concept evolution model;
training a learning algorithm with the training set and the concept evolution model to generate a trained model wherein the learning algorithm comprises a global approach to reshape a list of the classes and adjusts the set of features, or wherein the learning algorithm comprises a local approach that creates a local model of one or few events, the definition set of classes remains unchanged, and the training set can be extended with new examples;
using the trained model to predict class labels for unlabeled element instances within the document;
computing a confidence factor for a predicted class label is accurately predicted for unlabeled elements;
identifying an unlabeled element instance within the document with a corresponding suggested annotation having a confidence factor less than zero; and
adjusting the classifying of the unlabeled element instance wherein a second concept evolution model is composed for more accurate classifying of the document, and wherein the composing and applying are executed by a designer of the annotating model and the computing is machine implemented.
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Abstract
A method is provided for transforming data elements within a classification system based in part on input from a human annotator or expert. A first concept evolution model as a training set is composed from a first set of selectively determinable annotations and the first concept evolution model. A trained model is generated after training a learning algorithm with the training set and the concept evolution model. A confidence factor is computed that a predicted annotation is accurately identified. A selected element instance and a corresponding suggested annotation are identified to have a low confidence factor. The classifying of the applied annotation is adjusted where a second concept evolution model is composed for more accurate classifying of the data item.
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Citations
9 Claims
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1. A method for evolving an annotating model for classifying a document or a data item therein, comprising:
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composing a first concept evolution model as a training set comprised of a first set of selectively determinable class labels of element instances within the document that are detectable within the document to produce a result of predicting class labels to be assigned to unlabeled element instances and the first concept evolution model; training a learning algorithm with the training set and the concept evolution model to generate a trained model wherein the learning algorithm comprises a global approach to reshape a list of the classes and adjusts the set of features, or wherein the learning algorithm comprises a local approach that creates a local model of one or few events, the definition set of classes remains unchanged, and the training set can be extended with new examples; using the trained model to predict class labels for unlabeled element instances within the document; computing a confidence factor for a predicted class label is accurately predicted for unlabeled elements; identifying an unlabeled element instance within the document with a corresponding suggested annotation having a confidence factor less than zero; and adjusting the classifying of the unlabeled element instance wherein a second concept evolution model is composed for more accurate classifying of the document, and wherein the composing and applying are executed by a designer of the annotating model and the computing is machine implemented. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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Specification