# Effective Multi-Task Learning for Biomedical Named Entity Recognition

arXiv: https://arxiv.org/abs/2507.18542 · HTML: https://mlsanity.com/paper/2507.18542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 20 of 20)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [Automated Concatenation of Embeddings for Structured Prediction](https://mlsanity.com/paper/2010.05006/) (2020)
- [BioRED: A Rich Biomedical Relation Extraction Dataset](https://mlsanity.com/paper/2204.04263/) (2022)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Deep Joint Entity Disambiguation with Local Neural Attention](https://mlsanity.com/paper/1704.04920/) (2017)
- [Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.06804/) (2021)
- [Transition-Based Dependency Parsing with Stack Long Short-Term Memory](https://mlsanity.com/paper/1505.08075/) (2015)
- [A Sequence-to-Set Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2105.08901/) (2021)
- [Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning](https://mlsanity.com/paper/1801.09851/) (2018)
- [Biomedical named entity recognition using BERT in the machine reading comprehension framework](https://mlsanity.com/paper/2009.01560/) (2020)
- [Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study](https://mlsanity.com/paper/2401.10825/) (2024)
- [Parallel Instance Query Network for Named Entity Recognition](https://mlsanity.com/paper/2203.10545/) (2022)
- [MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers](https://mlsanity.com/paper/2001.08904/) (2020)
- [tmVar 3.0: an improved variant concept recognition and normalization tool](https://mlsanity.com/paper/2204.03637/) (2022)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation](https://mlsanity.com/paper/1909.11535/) (2019)
- [An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition](https://mlsanity.com/paper/2208.04534/) (2022)
- [Augmenting Biomedical Named Entity Recognition with General-domain Resources](https://mlsanity.com/paper/2406.10671/) (2024)

## Abstract (arXiv)

Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model's predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization.