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.

本研究利用短语嵌入搜索构建高覆盖率的实体词典，并利用其生成具有高覆盖率的命名实体识别（NER）数据集。通过利用字典中候选短语与目标实体类型之间的嵌入距离来减少噪声，实现了弱监督 NER 模型的改进。在 6 个 NER 基准测试中，与当前弱监督 NER 模型相比，HighGEN 表现出更为优异的性能。