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. c⃝2020 Association for Computational Linguistics 5849 A Unified MRC Framework for Named Entity Recognition Xiaoya Li♣, Jingrong Feng♣, Yuxian Meng♣, Qinghong Han♣, Fei Wu♠ and Jiwei Li♠♣ ♠ Department of Computer Science and Technology, Zhejiang University ♣ Shannon.AI {xiaoya li, jingrong feng, yuxian meng,qinghong han}@shannonai.com wufei@cs.zju.edu.cn, jiwei li@shannonai.com Abstract The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separately developed for the two tasks, since sequence la- beling models are only able to assign a single label to a particular token, which is unsuitable for nested NER where a token may be assigned several labels. In this paper, we propose a unified framework that is capable of handling both flat and nested NER tasks. Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehen- sion (MRC) task. For example, extracting en- tities with the PER(PERSON) label is formal- ized as extracting answer spans to the question “which person is mentioned in the text”.This formulation naturally tackles the entity over- lapping issue in nested NER: the extraction of two overlapping entities with different cat- egories requires answering two independent questions. Additionally, since the query en- codes informative prior knowledge, this strat- egy facilitates the process of entity extraction, leading to better performances for not only nested NER, but flat NER. We conduct experiments on both nested and flat NER datasets. Experiment re- sults demonstrate the effectiveness of the proposed formulation. We are able to achieve a vast amount of performance boost over current SOTA models on nested NER datasets, i.e., +1.28, +2.55, +5.44, +6.37,re- spectively on ACE04, ACE05, GENIA and KBP17, as well as flat NER datasets, i.e., +0.24, +1.95, +0.21, +1.49 respectively on English CoNLL 2003, English OntoNotes 5.0, Chinese MSRA and Chinese OntoNotes 4.0. The code and datasets can be found at https://github.com/ShannonAI/ mrc-for-flat-nested-ner. Figure 1: Examples for nested entities from GENIA and ACE04 corpora. 1 Introduction Named Entity Recognition (NER) refers to the task of detecting the span and the semantic cate- gory of entities from a chunk of text. The task can be further divided into two sub-categories, nested NER and flat NER, depending on whether entities are nested or not. Nested NER refers to a phe- nomenon that the spans of entities (mentions) are nested, as shown in Figure 1. Entity overlapping 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- nately incapable of handling overlapping entities in nested NER (Huang et al., 2015; Chiu and …

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