To ground this discussion, let's return to named entity recognition (NER). Given a scheme for representing spans and set of named entity types, a span-based approach to NER is a straightforward classification problem where each span in an input is assigned a class label. More formally, given an input sequence x, we want to assign a label y, from the set of valid NER labels, to each of the spans in S(x). Since most of the spans in a given input will not be named entities we'll add the label NULL to the set of types in Y . Figure 11 .10 A span-oriented approach to named entity classification. The figure only illustrates the computation for 2 spans corresponding to ground truth named entities. In reality, the network scores all of the