# Enhancing Label Consistency on Document-level Named Entity Recognition

arXiv: https://arxiv.org/abs/2210.12949 · HTML: https://mlsanity.com/paper/2210.12949/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning](https://mlsanity.com/paper/2105.03654/) (2021)
- [BERN2: an advanced neural biomedical named entity recognition and normalization tool](https://mlsanity.com/paper/2201.02080/) (2022)
- [Interpretable Multi-dataset Evaluation for Named Entity Recognition](https://mlsanity.com/paper/2011.06854/) (2020)
- [HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition](https://mlsanity.com/paper/2008.07347/) (2020)
- [Answering Questions on COVID-19 in Real-Time](https://mlsanity.com/paper/2006.15830/) (2020)
- [Regularization for Long Named Entity Recognition](https://mlsanity.com/paper/2104.07249/) (2021)
- [Larger-Context Tagging: When and Why Does It Work?](https://mlsanity.com/paper/2104.04434/) (2021)

## Built on by (most cited first, 1 of 1)

- [Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximization](https://mlsanity.com/paper/2305.14645/) (2023)

## Abstract (arXiv)

Named entity recognition (NER) is a fundamental part of extracting information from documents in biomedical applications. A notable advantage of NER is its consistency in extracting biomedical entities in a document context. Although existing document NER models show consistent predictions, they still do not meet our expectations. We investigated whether the adjectives and prepositions within an entity cause a low label consistency, which results in inconsistent predictions. In this paper, we present our method, ConNER, which enhances the label dependency of modifiers (e.g., adjectives and prepositions) to achieve higher label agreement. ConNER refines the draft labels of the modifiers to improve the output representations of biomedical entities. The effectiveness of our method is demonstrated on four popular biomedical NER datasets; in particular, its efficacy is proved on two datasets with 7.5-8.6% absolute improvements in the F1 score. We interpret that our ConNER method is effective on datasets that have intrinsically low label consistency. In the qualitative analysis, we demonstrate how our approach makes the NER model generate consistent predictions. Our code and resources are available at https://github.com/dmis-lab/ConNER/.