  Information Extraction refers to a collection of tasks within Natural
Language Processing (NLP) that identifies sub-sequences within text and their
labels. These tasks have been used for many years to link extract relevant
information and to link free text to structured data. However, the
heterogeneity among information extraction tasks impedes progress in this area.
We therefore offer a unifying perspective centered on what we define to be
spans in text. We then re-orient these seemingly incongruous tasks into this
unified perspective and then re-present the wide assortment of information
extraction tasks as variants of the same basic Span-Oriented Information
Extraction task.
