# SpanNER: Named Entity Re-/Recognition as Span Prediction

arXiv: https://arxiv.org/abs/2106.00641 · HTML: https://mlsanity.com/paper/2106.00641/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 65 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [ExplainaBoard: An Explainable Leaderboard for NLP](https://mlsanity.com/paper/2104.06387/) (2021)
- [Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study](https://mlsanity.com/paper/2001.03844/) (2020)
- [Hierarchical Contextualized Representation for Named Entity Recognition](https://mlsanity.com/paper/1911.02257/) (2019)
- [TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition](https://mlsanity.com/paper/2004.07493/) (2020)
- [Interpretable Multi-dataset Evaluation for Named Entity Recognition](https://mlsanity.com/paper/2011.06854/) (2020)
- [Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition](https://mlsanity.com/paper/2004.14514/) (2020)
- [Generalizing Natural Language Analysis through Span-relation Representations](https://mlsanity.com/paper/1911.03822/) (2019)
- [Multi-Grained Named Entity Recognition](https://mlsanity.com/paper/1906.08449/) (2019)
- [GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition](https://mlsanity.com/paper/1907.05611/) (2019)
- [Robust Lexical Features for Improved Neural Network Named-Entity Recognition](https://mlsanity.com/paper/1806.03489/) (2018)
- [Neural System Combination for Machine Translation](https://mlsanity.com/paper/1704.06393/) (2017)
- [Larger-Context Tagging: When and Why Does It Work?](https://mlsanity.com/paper/2104.04434/) (2021)

## Built on by (most cited first, 30 of 55)

- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases](https://mlsanity.com/paper/2210.12925/) (2022)
- [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling](https://mlsanity.com/paper/2110.02001/) (2021)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2204.05751/) (2022)
- [MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective](https://mlsanity.com/paper/2204.04391/) (2022)
- [Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes](https://mlsanity.com/paper/2208.08023/) (2022)
- [Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition](https://mlsanity.com/paper/2208.03054/) (2022)
- [Post-Training Large Language Models via Reinforcement Learning from Self-Feedback](https://mlsanity.com/paper/2507.21931/) (2025)
- [Rethinking Negative Instances for Generative Named Entity Recognition](https://mlsanity.com/paper/2402.16602/) (2024)
- [LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty](https://mlsanity.com/paper/2402.10573/) (2024)
- [PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)](https://mlsanity.com/paper/2507.02966/) (2025)
- [What do we Really Know about State of the Art NER?](https://mlsanity.com/paper/2205.00034/) (2022)
- [Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning](https://mlsanity.com/paper/2305.13628/) (2023)
- [ToNER: Type-oriented Named Entity Recognition with Generative Language Model](https://mlsanity.com/paper/2404.09145/) (2024)
- [Zero-shot Triplet Extraction by Template Infilling](https://mlsanity.com/paper/2212.10708/) (2022)
- [E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition](https://mlsanity.com/paper/2305.17854/) (2023)
- [A Neural Span-Based Continual Named Entity Recognition Model](https://mlsanity.com/paper/2302.12200/) (2023)
- [Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications](https://mlsanity.com/paper/2310.19942/) (2023)
- [Span Classification with Structured Information for Disfluency Detection in Spoken Utterances](https://mlsanity.com/paper/2203.16028/) (2022)
- [Less than One-shot: Named Entity Recognition via Extremely Weak Supervision](https://mlsanity.com/paper/2311.02861/) (2023)
- [REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2206.05123/) (2022)
- [DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2211.08104/) (2022)
- [Emotion-Cause Pair Extraction as Question Answering](https://mlsanity.com/paper/2301.01982/) (2023)
- [Span-based Named Entity Recognition by Generating and Compressing Information](https://mlsanity.com/paper/2302.05392/) (2023)
- [A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition](https://mlsanity.com/paper/2305.12485/) (2023)
- [LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints](https://mlsanity.com/paper/2309.15458/) (2023)

## Abstract (arXiv)

Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span prediction model's architectural bias has not been fully understood. In this paper, we first investigate the strengths and weaknesses when the span prediction model is used for named entity recognition compared with the sequence labeling framework and how to further improve it, which motivates us to make complementary advantages of systems based on different paradigms. We then reveal that span prediction, simultaneously, can serve as a system combiner to re-recognize named entities from different systems' outputs. We experimentally implement 154 systems on 11 datasets, covering three languages, comprehensive results show the effectiveness of span prediction models that both serve as base NER systems and system combiners. We make all code and datasets available: urlhttps://github.com/neulab/spanner, as well as an online system demo: urlhttp://spanner.sh. Our model also has been deployed into the ExplainaBoard platform, which allows users to flexibly perform a system combination of top-scoring systems in an interactive way: urlhttp://explainaboard.nlpedia.ai/leaderboard/task-ner/.