  Fine-grained Named Entity Recognition is a task whereby we detect and
classify entity mentions to a large set of types. These types can span diverse
domains such as finance, healthcare, and politics. We observe that when the
type set spans several domains the accuracy of the entity detection becomes a
limitation for supervised learning models. The primary reason being the lack of
datasets where entity boundaries are properly annotated, whilst covering a
large spectrum of entity types. Furthermore, many named entity systems suffer
when considering the categorization of fine grained entity types. Our work
attempts to address these issues, in part, by combining state-of-the-art deep
learning models (ELMo) with an expansive knowledge base (Wikidata). Using our
framework, we cross-validate our model on the 112 fine-grained entity types
based on the hierarchy given from the Wiki(gold) dataset.
