Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations

Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, Jaewoo Kang

Abstract
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.
Anthology ID:
2023.acl-long.394
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7148–7163
Language:
URL:
https://aclanthology.org/2023.acl-long.394
DOI:
10.18653/v1/2023.acl-long.394
Bibkey:
Cite (ACL):
Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, and Jaewoo Kang. 2023. Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7148–7163, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations (Kim et al., ACL 2023)
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PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/2023.acl-long.394.pdf
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