Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations

• URL: http://arxiv.org/abs/2210.07586v4
• Date: Thu, 1 Jun 2023 06:26:46 GMT
• Title: Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations
• Authors: Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, Jaewoo Kang
• Abstract summary: Most weakly supervised named entity recognition models rely on domain-specific dictionaries provided by experts. We present a novel framework, HighGEN, that generates NER datasets with high-coverage pseudo-dictionaries. We demonstrate that HighGEN outperforms the previous best model by an average F1 score of 4.7 across five NER benchmark datasets.
• Score: 20.00016240535205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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