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.

翻译：大多数弱监督命名实体识别（NER）模型依赖专家提供的领域特定词典。在缺乏此类词典的诸多领域中，这一方法难以实施。尽管近期研究通过短语检索模型从维基百科自动检索实体构建伪词典，但此类词典往往覆盖范围有限，因为检索器更倾向于检索流行实体而非罕见实体。本研究提出新型框架HighGEN，通过高覆盖伪词典生成NER数据集。具体而言，我们采用名为短语嵌入搜索的新型搜索方法构建富含实体的词典，该方法能引导检索器在密集包含各类实体的空间中进行探索。此外，我们基于候选实体提及与实体类型之间的嵌入距离设计新型验证流程，有效降低高覆盖词典所生成弱标签中的假阳性噪声。实验表明，在五个NER基准数据集上，HighGEN较先前最优模型平均F1值提升4.7。