5. A method implemented in a computing device, the method comprising: receiving a named entity input from a source; identifying a target sense for the named entity input, wherein the target sense is a particular desired usage of the named entity input in a set of documents; and generating by the computing device, based at least in part on both the named entity input and the set of documents, an extraction complexity measurement that indicates how difficult it is deemed to be to identify the named entity input in the set of documents for the target sense, the generating the extraction complexity measurement comprising: performing a graph-based spreading activation technique to generate a language model by: building an undirected graph based on the named entity input and the set of documents, the undirected graph including multiple vertices and multiple edges, incrementing 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 normalizing, after incrementing the scores of the selected ones of the multiple vertices, scores of the multiple vertices to obtain the language model; performing a graph-based clustering technique to refine the language model, wherein the refined language model includes multiple clusters; and determining the extraction complexity measurement based on the refined language model, wherein the determining the extraction complexity measurement comprises identifying a number of documents in clusters of the multiple clusters having a score greater than an average cluster score.
5. A method implemented in a computing device, the method comprising: receiving a named entity input from a source; identifying a target sense for the named entity input, wherein the target sense is a particular desired usage of the named entity input in a set of documents; and generating by the computing device, based at least in part on both the named entity input and the set of documents, an extraction complexity measurement that indicates how difficult it is deemed to be to identify the named entity input in the set of documents for the target sense, the generating the extraction complexity measurement comprising: performing a graph-based spreading activation technique to generate a language model by: building an undirected graph based on the named entity input and the set of documents, the undirected graph including multiple vertices and multiple edges, incrementing 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 normalizing, after incrementing the scores of the selected ones of the multiple vertices, scores of the multiple vertices to obtain the language model; performing a graph-based clustering technique to refine the language model, wherein the refined language model includes multiple clusters; and determining the extraction complexity measurement based on the refined language model, wherein the determining the extraction complexity measurement comprises identifying a number of documents in clusters of the multiple clusters having a score greater than an average cluster score. 6. A method as recited in claim 5 , further comprising providing the extraction complexity measurement to a named entity recognition module that identifies the named entity input in the set of documents based at least in part on the extraction complexity measurement.