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Most weakly supervised named entity recognition (NER) models rely ondomain-specific dictionaries provided by experts. This approach is infeasiblein many domains where dictionaries do not exist. While a phrase retrieval modelwas used to construct pseudo-dictionaries with entities retrieved fromWikipedia automatically in a recent study, these dictionaries often havelimited coverage because the retriever is likely to retrieve popular entitiesrather 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 aspace densely populated with various entities. In addition, we use a newverification process based on the embedding distance between candidate entitymentions and entity types to reduce the false-positive noise in weak labelsgenerated by high-coverage dictionaries. We demonstrate that HighGENoutperforms the previous best model by an average F1 score of 4.7 across fiveNER benchmark datasets.

Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations