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.