# TURNER: The Uncertainty-based Retrieval Framework for Chinese NER

arXiv: https://arxiv.org/abs/2202.09022 · HTML: https://mlsanity.com/paper/2202.09022/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [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)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [Chinese NER Using Lattice LSTM](https://mlsanity.com/paper/1805.02023/) (2018)
- [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021)
- [A Retrieve-and-Edit Framework for Predicting Structured Outputs](https://mlsanity.com/paper/1812.01194/) (2018)
- [TENER: Adapting Transformer Encoder for Named Entity Recognition](https://mlsanity.com/paper/1911.04474/) (2019)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter](https://mlsanity.com/paper/2105.07148/) (2021)
- [Uncertainty-Aware Label Refinement for Sequence Labeling](https://mlsanity.com/paper/2012.10608/) (2020)
- [Neural Reranking for Named Entity Recognition](https://mlsanity.com/paper/1707.05127/) (2017)
- [DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling](https://mlsanity.com/paper/2109.08818/) (2021)

## Built on by (most cited first, 4 of 4)

- [Domain-Specific NER via Retrieving Correlated Samples](https://mlsanity.com/paper/2208.12995/) (2022)
- [Universal Information Extraction with Meta-Pretrained Self-Retrieval](https://mlsanity.com/paper/2306.10444/) (2023)
- [The Uncertainty-based Retrieval Framework for Ancient Chinese CWS and POS](https://mlsanity.com/paper/2310.08496/) (2023)
- [Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges](https://mlsanity.com/paper/2211.00301/) (2022)

## Abstract (arXiv)

Chinese NER is a difficult undertaking due to the ambiguity of Chinese characters and the absence of word boundaries. Previous work on Chinese NER focus on lexicon-based methods to introduce boundary information and reduce out-of-vocabulary (OOV) cases during prediction. However, it is expensive to obtain and dynamically maintain high-quality lexicons in specific domains, which motivates us to utilize more general knowledge resources, e.g., search engines. In this paper, we propose TURNER: The Uncertainty-based Retrieval framework for Chinese NER. The idea behind TURNER is to imitate human behavior: we frequently retrieve auxiliary knowledge as assistance when encountering an unknown or uncertain entity. To improve the efficiency and effectiveness of retrieval, we first propose two types of uncertainty sampling methods for selecting the most ambiguous entity-level uncertain components of the input text. Then, the Knowledge Fusion Model re-predict the uncertain samples by combining retrieved knowledge. Experiments on four benchmark datasets demonstrate TURNER's effectiveness. TURNER outperforms existing lexicon-based approaches and achieves the new SOTA.