# DMNER: Biomedical Entity Recognition by Detection and Matching

arXiv: https://arxiv.org/abs/2306.15736 · HTML: https://mlsanity.com/paper/2306.15736/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 27 Jun 2023.

## Builds on (most cited first, 21 of 21)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (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)
- [GPT-NER: Named Entity Recognition via Large Language Models](https://mlsanity.com/paper/2304.10428/) (2023)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Learning Named Entity Tagger using Domain-Specific Dictionary](https://mlsanity.com/paper/1809.03599/) (2018)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [Nested Named Entity Recognition via Second-best Sequence Learning and Decoding](https://mlsanity.com/paper/1909.02250/) (2019)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Low-Resource Name Tagging Learned with Weakly Labeled Data](https://mlsanity.com/paper/1908.09659/) (2019)
- [Empower Sequence Labeling with Task-Aware Neural Language Model](https://mlsanity.com/paper/1709.04109/) (2017)
- [Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning](https://mlsanity.com/paper/1801.09851/) (2018)
- [Named Entity Recognition with Partially Annotated Training Data](https://mlsanity.com/paper/1909.09270/) (2019)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [Multi-Grained Named Entity Recognition](https://mlsanity.com/paper/1906.08449/) (2019)
- [Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation](https://mlsanity.com/paper/1909.11535/) (2019)
- [HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition](https://mlsanity.com/paper/1912.01731/) (2019)

## Built on by (most cited first, 2 of 2)

- [Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition](https://mlsanity.com/paper/2309.12278/) (2023)
- [Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach](https://mlsanity.com/paper/2608.08636/) (2026)

## Abstract (arXiv)

Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAPBERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER.