  Most weakly supervised named entity recognition (NER) models rely on
domain-specific dictionaries provided by experts. This approach is infeasible
in many domains where dictionaries do not exist. While a phrase retrieval model
was used to construct pseudo-dictionaries with entities retrieved from
Wikipedia automatically in a recent study, these dictionaries often have
limited coverage because the retriever is likely to retrieve popular entities
rather than rare ones. In this study, we present a novel framework, HighGEN,
that generates NER datasets with high-coverage pseudo-dictionaries.
Specifically, we create entity-rich dictionaries with a novel search method,
called phrase embedding search, which encourages the retriever to search a
space densely populated with various entities. In addition, we use a new
verification process based on the embedding distance between candidate entity
mentions and entity types to reduce the false-positive noise in weak labels
generated by high-coverage dictionaries. We demonstrate that HighGEN
outperforms the previous best model by an average F1 score of 4.7 across five
NER benchmark datasets.
