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Named Entity Recognition with Extremely Limited Data
[article]
2018
arXiv
pre-print
Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity taggers themselves are typically trained on thousands or tens of thousands of examples labeled by humans. However, there is a long tail of named entities classes, and for these cases, labeled data may be impossible to find or justify financially. We propose exploring named entity recognition as a search task, where
arXiv:1806.04411v2
fatcat:jrbtrg26oja6nduxmzmhmijkbu