6 research outputs found
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations
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.Comment: ACL 202
Simple Questions Generate Named Entity Recognition Datasets
Recent named entity recognition (NER) models often rely on human-annotated
datasets requiring the vast engagement of professional knowledge on the target
domain and entities. This work introduces an ask-to-generate approach, which
automatically generates NER datasets by asking simple natural language
questions to an open-domain question answering system (e.g., "Which disease?").
Despite using fewer training resources, our models solely trained on the
generated datasets largely outperform strong low-resource models by 20.8 F1
score on average across six popular NER benchmarks. Our models also show
competitive performance with rich-resource models that additionally leverage
in-domain dictionaries provided by domain experts. In few-shot NER, we
outperform the previous best model by 5.2 F1 score on three benchmarks and
achieve new state-of-the-art performance.Comment: Code available at https://github.com/dmis-lab/GeNE