While the end-to-end model developed by<cite> (Lample et al., 2016)</cite> clearly includes information comparable to that in the CLM, it requires a fully annotated NER corpus, takes significant time and computational resources to train, and is non-trivial to integrate into a new NER system. The CLM approach captures a very large fraction of the entity/non-entity distinction capacity of full NER systems, and can be rapidly trained using only entity and non-entity token lists -i.e., it is corpus-agnostic.