Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations
This addresses the challenge of creating NER datasets in domains lacking expert dictionaries, though it is incremental as it builds on prior phrase retrieval methods.
The paper tackles the problem of limited coverage in weakly supervised named entity recognition (NER) datasets by introducing HighGEN, a framework that generates high-coverage pseudo-dictionaries using phrase embedding search and a verification process, resulting in an average F1 score improvement of 4.7 across five benchmark datasets.
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.