A program that learns by recording cases generally makes use of consistency heuristic. According to consistency heuristic, a property of something can be guessed by finding the most similar cases from a given set of cases. For example, a computer is given the images of different types of insects, birds, and animals. If the computer is asked to identify a living thing which is not in the recorded list, it will compare the given image with already recorded ones, and will at least tell whether the given image is insect, bird or animal.
Learning by recoding cases technique is mainly used in natural language learning tasks.
During the training phase, a set of cases that describe ambiguity resolution episodes for a particular problem in text analysis is collected. Each case contains a set of features or attribute-value pairs that encode the context in which the ambiguity was encountered.
Moreover, each case is annotated with solution features that explain how the ambiguity was resolved in the current example. The cases which are created are then stored in a case base. Once the training is over, the system can use the case base to resolve ambiguities in new sentences. This way, the system acquires the linguistic knowledge.