 Sequence labeling can be treated as a set of independent classification tasks, one per member of the sequence.However, accuracy is generally improved by making the optimal label for a given element dependent on the choices of nearby elements, using special algorithms to choose the "globally" best set of labels for the entire sequence at once.As an example of why finding the globally best label sequence might produce better results than labeling one item at a time, consider the part-of-speech tagging task just described.