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Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations

2022-10-14 07:36:44
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, a phrase embedding search to efficiently create high-coverage dictionaries is presented. Specifically, the reformulation of natural language queries into phrase representations allows the retriever to search a space densely populated with various entities. In addition, we present a novel framework, HighGEN, that generates NER datasets with high-coverage dictionaries obtained using the phrase embedding search. HighGEN generates weak labels based on the distance between the embeddings of a candidate phrase and target entity type to reduce the noise in high-coverage dictionaries. We compare HighGEN with current weakly supervised NER models on six NER benchmarks and demonstrate the superiority of our models.

Abstract (translated)

URL

https://arxiv.org/abs/2210.07586

PDF

https://arxiv.org/pdf/2210.07586.pdf


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