11. One or more computer storage media having stored thereon multiple instructions that, when executed by one or more processors of a computing device, cause the one or more processors to: receive a named entity input from a source; identify a target sense for the named entity input, wherein the target sense is a particular desired usage of the named entity input in a document set; and generate, based at least in part on both the named entity input and the document set, an extraction complexity measurement that indicates a complexity of identifying the named entity input in the document set for the target sense, wherein to generate the extraction complexity measurement is to build an undirected graph based on the named entity input and the document set, the undirected graph including multiple vertices and multiple edges, the multiple vertices comprising co-occurring contexts surrounding occurrences of the named entity input in the set of documents.
11. One or more computer storage media having stored thereon multiple instructions that, when executed by one or more processors of a computing device, cause the one or more processors to: receive a named entity input from a source; identify a target sense for the named entity input, wherein the target sense is a particular desired usage of the named entity input in a document set; and generate, based at least in part on both the named entity input and the document set, an extraction complexity measurement that indicates a complexity of identifying the named entity input in the document set for the target sense, wherein to generate the extraction complexity measurement is to build an undirected graph based on the named entity input and the document set, the undirected graph including multiple vertices and multiple edges, the multiple vertices comprising co-occurring contexts surrounding occurrences of the named entity input in the set of documents. 14. One or more computer storage media as recited in claim 11 , wherein to generate the extraction complexity measurement is further to: increment scores of selected ones of the multiple vertices by propagating a relevance of one or more of the multiple vertices through the undirected graph; and normalize, after the scores of the selected ones of the multiple vertices are incremented, scores of the multiple vertices to obtain a language model.