%0 Conference Proceedings %T Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification %A Aly, Rami %A Vlachos, Andreas %A McDonald, Ryan %Y Zong, Chengqing %Y Xia, Fei %Y Li, Wenjie %Y Navigli, Roberto %S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F aly-etal-2021-leveraging %X A common issue in real-world applications of named entity recognition and classification (NERC) is the absence of annotated data for the target entity classes during training. Zero-shot learning approaches address this issue by learning models from classes with training data that can predict classes without it. This paper presents the first approach for zero-shot NERC, introducing novel architectures that leverage the fact that textual descriptions for many entity classes occur naturally. We address the zero-shot NERC specific challenge that the not-an-entity class is not well defined as different entity classes are considered in training and testing. For evaluation, we adapt two datasets, OntoNotes and MedMentions, emulating the difficulty of real-world zero-shot learning by testing models on the rarest entity classes. Our proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. Furthermore, we assess the effect of different class descriptions for this task. %R 10.18653/v1/2021.acl-long.120 %U https://aclanthology.org/2021.acl-long.120 %U https://doi.org/10.18653/v1/2021.acl-long.120 %P 1516-1528