# BioMNER: A Dataset for Biomedical Method Entity Recognition

arXiv: https://arxiv.org/abs/2406.20038 · HTML: https://mlsanity.com/paper/2406.20038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 28 Jun 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Effective Distillation of Table-based Reasoning Ability from LLMs](https://mlsanity.com/paper/2309.13182/) (2023)
- [EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention](https://mlsanity.com/paper/2210.12463/) (2022)
- [TwistList: Resources and Baselines for Tongue Twister Generation](https://mlsanity.com/paper/2306.03457/) (2023)
- [Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation](https://mlsanity.com/paper/2306.16195/) (2023)
- [NGEP: A Graph-based Event Planning Framework for Story Generation](https://mlsanity.com/paper/2210.10602/) (2022)
- [SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation](https://mlsanity.com/paper/2405.15924/) (2024)
- [Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics](https://mlsanity.com/paper/2210.10618/) (2022)
- [Improving Medical Dialogue Generation with Abstract Meaning Representations](https://mlsanity.com/paper/2309.10608/) (2023)
- [Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers](https://mlsanity.com/paper/2310.15684/) (2023)
- [Enhancing Biomedical Lay Summarisation with External Knowledge Graphs](https://mlsanity.com/paper/2310.15702/) (2023)
- [A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation](https://mlsanity.com/paper/2311.11271/) (2023)
- [Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases](https://mlsanity.com/paper/2403.13901/) (2024)
- [Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation](https://mlsanity.com/paper/2404.01129/) (2024)

## Abstract (arXiv)

Named entity recognition (NER) stands as a fundamental and pivotal task within the realm of Natural Language Processing. Particularly within the domain of Biomedical Method NER, this task presents notable challenges, stemming from the continual influx of domain-specific terminologies in scholarly literature. Current research in Biomedical Method (BioMethod) NER suffers from a scarcity of resources, primarily attributed to the intricate nature of methodological concepts, which necessitate a profound understanding for precise delineation. In this study, we propose a novel dataset for biomedical method entity recognition, employing an automated BioMethod entity recognition and information retrieval system to assist human annotation. Furthermore, we comprehensively explore a range of conventional and contemporary open-domain NER methodologies, including the utilization of cutting-edge large-scale language models (LLMs) customised to our dataset. Our empirical findings reveal that the large parameter counts of language models surprisingly inhibit the effective assimilation of entity extraction patterns pertaining to biomedical methods. Remarkably, the approach, leveraging the modestly sized ALBERT model (only 11MB), in conjunction with conditional random fields (CRF), achieves state-of-the-art (SOTA) performance.