A Unified MRC Framework for Named Entity Recognition

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5849 5859 July 5 - 10, 2020 Association for Computational Linguistics5849A Unified MRC Framework for Named Entity RecognitionXiaoya Li , Jingrong Feng , Yuxian Meng , Qinghong Han , Fei Wu and Jiwei Li Department of Computer Science and Technology, Zhejiang University {xiaoyali, jingrongfeng, task of Named Entity Recognition (NER)is normally divided into nested NER and flatNER depending on whether Named entities arenested or not. Models are usually separatelydeveloped for the two tasks, since sequence la-beling models are only able to assign a singlelabel to a particular token, which is unsuitablefor nested NER where a token may be assignedseveral this paper, we propose a Unified frameworkthat is capable of handling both flat and nestedNER tasks.}

is a fairly common phenomenon in natural lan-guages. The task of flat NER is commonly formalized as a sequence labeling task: a sequence labeling model (Chiu and Nichols,2016;Ma and Hovy, 2016;Devlin et al.,2018) is trained to assign a single tagging class to each unit within a se-quence of tokens. This formulation is unfortu-

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