Fine-Grained Named Entity Recognition using ELMo and Wikidata
This addresses the challenge of detecting and classifying entities with many types for applications in domains like finance and healthcare, but it is incremental as it builds on existing methods.
The paper tackled the problem of low accuracy in fine-grained named entity recognition across multiple domains by combining ELMo with Wikidata, achieving cross-validation on 112 entity types from the Wiki(gold) dataset.
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.