Relation Extraction

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Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?
Jun 13, 2026Jakob Mraz, Tomaž Curk, Blaž ZupanRelation Extraction Few-Shot Learning
University of Ljubljana, Ljubljana, Slovenia · Baylor College of Medicine, Houston, Texas, USA
Applicability Condition Extraction for Therapeutic Drug-Disease Relations
Jun 12, 2026Guanting Luo, Noriki Nishida, Yuji Matsumoto +1Relation Extraction
1The University of Osaka · 2RIKEN · 4Tohoku University +1
Towards Accurate Emotion-Attributed Video Captioning via Fine-grained Emotion-Cause Pair Extraction
Jun 7, 2026Weidong Chen, Cheng Ye, Zhendong Mao +3Relation Extraction Video Captioning
School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
UniD $^3$ : A Knowledge Graph-Enhanced RAG Framework for Drug-Disease Discovery and Reasoning
May 31, 2026Qing Wang, Tianshi Liu, Minghao Zhou +5Relation Extraction KG Construction
Department of Health Outcomes and Biomedical Informatics, University of Florida, Florida, USA · Department of Hematology, H. Lee Moffitt Cancer Center and Research Institute, Florida, USA · Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA +2
PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text
May 27, 2026Ifeoluwa Kunle-John, Josiah Paul, Oluwatosin Agbaakin +3Relation Extraction Clinical NLP
Edyah Limited1, University of Ibadan, NG2 · Indiana University, Indianapolis, IN3 · Creighton University, Omaha NE4
Snippet-Driven Supply Chain Discovery with LLMs: Scaling Visibility in China
May 27, 2026Hiroto Fukada, Takayuki MizunoRelation Extraction KG Construction
Graduate Institute for Advanced Studies (SOKENDAI) · National Institute of Informatics · Tokyo, Japan
Reliable Extraction of Clinical Follow-Up Instructions: A Hybrid Neural-Symbolic Pipeline
May 26, 2026Michal Laufer, Yehudit Aperstein, Alexander ApartsinRelation Extraction Healthcare
1Bar-Ilan University, Ramat Gan, Israel · 2Afeka College of Engineering, Tel Aviv, Israel · 3Holon Institute of Technology, Holon, Israel
They Are Not the Same: Direct Causes Are Not Grounded Emotion Explanations
May 24, 2026Zhuangzhuang Pan, Yan Xia, Chee Seng ChanRelation Extraction Causal Attribution
Universiti Malaya, Malaysia · Suzhou University of Technology, China · VinUniversity, Vietnam
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
May 22, 2026Jiawei He, Mengyu Shi, Jiawei Liu +6Relation Extraction Synthetic Data Augmentation
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China · Amap, Alibaba Group, China · University of Alberta, Edmonton, Canada +1
SAVER: Selective As-Needed Vision Evidence for Multimodal Information Extraction
May 20, 2026Miaobo Hu, Shuhao Hu, Bokun Wang +5Relation Extraction Named Entity Recognition
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100092, China · School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs
May 15, 2026Inoussa Mouiche, sherif SaadRelation Extraction KG Construction
School of Computer Science, University of Windsor, ON, Canada
Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence
May 12, 2026Ujun Jeong, Saketh Vishnubhatla, Bohan Jiang +3Relation Extraction LLM Evaluation
Arizona State University, Tempe, Arizona, USA · DEVCOM Army Research Laboratory, Adelphi, Maryland, USA
GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction
May 11, 2026Ihor Stepanov, Oleksandr Lukashov, Mykhailo Shtopko +1Relation Extraction KG Construction
Knowledgator Engineering, Kyiv, Ukraine · Baldor Technologies Pvt. Ltd. (IDfy), Mumbai, India
TIJERE: A Novel Threat Intelligence Joint Extraction Model Based on Analyst Expert Knowledge
May 3, 2026Inoussa Mouiche, Sherif SaadRelation Extraction Named Entity Recognition
School of Computer Science, University of Windsor, 401 Sunset Ave, Windsor, N9B 3P4, ON, Canada
Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques
Apr 30, 2026Weikang Yang, S M Mazharul Hoque Chowdhury, Wei JinRelation Extraction Healthcare
Department of Computer Science, North Dakota State University, Fargo, USA · Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh · Department of Computer Science and Engineering, University of North Texas, Denton, USA
Looking for the Bottleneck in Fine-grained Temporal Relation Classification
Apr 27, 2026Hugo Sousa, Ricardo Campos, Alípio JorgeRelation Extraction
University of Porto · INESC TEC · Porto, Portugal +1
Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment
Apr 21, 2026Tianxiang Ma, Weijie Feng, Xinyu Wang +1Relation Extraction Emotion Recognition in Conversations
Hefei University of Technology
ClimateCause: Complex and Implicit Causal Structures in Climate Reports
Apr 16, 2026Liesbeth Allein, Nataly Pineda-Castañeda, Andrea Rocci +1Relation Extraction Climate Science
Department of Computer Science, KU Leuven, Belgium · Institute of Argumentation, Linguistics, and Semiotics (IALS), Universit`a della Svizzera italiana, Switzerland
RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs
Mar 9, 2026Zhijun Wang, Ling Luo, Dinghao Pan +4Relation Extraction Clinical Information Extraction
School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Liaoning 116024, China · Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital & Institute, No.44 Xiaoheyan Road, Dadong District, Liaoning 110042, China
CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
Feb 19, 2026Juri Opitz, Corina Raclé, Emanuela Boros +4Relation Extraction Historical Document Analysis
University of Zurich, Switzerland · École Polytechnique Fédérale de Lausanne (EPFL), Switzerland · Swiss Art Research Infrastructure (SARI), University of Zurich
Agentic AI for Clustering, Relationship Discovery, and Semantic Trading in Prediction Markets
Dec 2, 2025Agostino Capponi, Alfio Gliozzo, Brian ZhuRelation Extraction Clustering
Columbia University, Columbia Business School · IBM · Columbia University
SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Sep 9, 2025Decheng Duan, Yingyi Zhang, Jitong Peng +1Relation Extraction Named Entity Recognition
Nanjing University of Science and Technology, China · Soochow University, China

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Few-Shot Biomedical Relation Extraction with Large Language Models: A Viable Alternative to Supervised Learning?

Jakob Mraz, Tomaž Curk, Blaž Zupan

University of Ljubljana, Ljubljana, Slovenia · Baylor College of Medicine, Houston, Texas, USA

Relation Extraction Few-Shot Learning Few-Shot Prompting LLM Information Extraction

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Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge. However, most existing approaches rely on supervised models trained on costly annotated datasets, limiting their scalability and adaptability across relation types and domains. We investigate few-shot BioRE using prompt-based learning with large language models (LLMs) and compare two task formulations: pairwise classification, which predicts relations for individual entity pairs, and joint generation, which extracts multiple relations in a single model call. Experiments on the BioREDirect dataset reveal a clear precision-recall trade-off. Pairwise classification achieves higher recall, whereas joint generation is more precise and computationally efficient. The best-performing model achieves a micro-F1 score of 0.44, substantially outperforming previous few-shot results (0.34) while remaining below the supervised baseline (0.56). Much of this gap is attributable to a single ambiguously defined relation type. When evaluated using macro-F1, which better captures performance across relation types in an imbalanced setting, prompt-based approaches outperform the supervised baseline (0.45 vs. 0.38), particularly on rare relation types. These findings highlight the potential of LLMs for BioRE in low-resource settings and underscore the importance of well-defined relation schemas.

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