Relation_Extraction
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A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text
2026-03-08 06:16:35Fei Cheng, Ribeka Tanaka, Sadao KurohashiAbstract
Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end-to-end system to jointly optimize three-stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in-domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach could serve as a strong joint baseline for future research. The code is publicly available.
Abstract (translated)
临床信息提取（例如，2010年i2b2/VA挑战赛）通常包括概念识别、断言分类和关系抽取等任务。在临床领域中联合建模多阶段任务是一个较少被探索的课题。现有的独立任务设置（每个阶段都有给定的参考输入），使得联合模型无法直接与现有流水线工作进行比较。为了解决这些问题，我们定义了一个联合任务设置，并提出了一种新颖的端到端系统以共同优化三个阶段的任务。我们通过使用各种嵌入技术：词嵌入、上下文嵌入和领域内上下文嵌入来进行实验性评估，比较了我们的提案与流水线基线的联合评价结果。提出的联合系统在概念、断言和关系F1分数上分别比流水线基线高出0.3、1.4和3.1分。这项工作将联合方法与临床信息提取联系起来，所提出的方法可以作为未来研究的强大联合基准。代码已公开发布。
URL
https://arxiv.org/abs/2603.07487
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Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI
2026-03-06 14:50:08Tanjim Taharat AurpaAbstract
Mathematical text understanding is a challenging task due to the presence of specialized entities and complex relationships between them. This study formulates mathematical problem interpretation as a Mathematical Entity Relation Extraction (MERE) task, where operands are treated as entities and operators as their relationships. Transformer-based models are applied to automatically extract these relations from mathematical text, with Bidirectional Encoder Representations from Transformers (BERT) achieving the best performance, reaching an accuracy of 99.39%. To enhance transparency and trust in the model's predictions, Explainable Artificial Intelligence (XAI) is incorporated using Shapley Additive Explanations (SHAP). The explainability analysis reveals how specific textual and mathematical features influence relation prediction, providing insights into feature importance and model behavior. By combining transformer-based learning, a task-specific dataset, and explainable modeling, this work offers an effective and interpretable framework for MERE, supporting future applications in automated problem solving, knowledge graph construction, and intelligent educational systems.
Abstract (translated)
数学文本理解是一个具有挑战性的任务，因为其中包含专业的实体以及这些实体之间复杂的关系。这项研究将数学问题解释定义为一个数学实体关系抽取（MERE）的任务，在这个过程中，操作数被视为实体，而运算符则作为它们之间的关系。基于Transformer的模型被应用于自动从数学文本中提取这些关系，并且双向编码器表示法（BERT）表现最佳，达到了99.39%的准确率。 为了提高模型预测的透明度和可信度，研究采用了可解释的人工智能（XAI），具体使用了Shapley Additive Explanations（SHAP）。通过这种可解释性分析，可以揭示特定文本特征和数学特性如何影响关系预测，并提供有关特征重要性和模型行为的见解。 这项工作结合了基于Transformer的学习、任务特异性数据集以及可解释建模方法，为MERE提供了有效且可解释的框架。这将支持未来在自动化问题解决、知识图谱构建以及智能教育系统中的应用。
URL
https://arxiv.org/abs/2603.06348
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A Study on Building Efficient Zero-Shot Relation Extraction Models
2026-03-01 20:44:31Hugo Thomas, Caio Corro, Guillaume Gravier, Pascale S\'ebillotAbstract
Zero-shot relation extraction aims to identify relations between entity mentions using textual descriptions of novel types (i.e., previously unseen) instead of labeled training examples. Previous works often rely on unrealistic assumptions: (1) pairs of mentions are often encoded directly in the input, which prevents offline pre-computation for large scale document database querying; (2) no rejection mechanism is introduced, biasing the evaluation when using these models in a retrieval scenario where some (and often most) inputs are irrelevant and must be ignored. In this work, we study the robustness of existing zero-shot relation extraction models when adapting them to a realistic extraction scenario. To this end, we introduce a typology of existing models, and propose several strategies to build single pass models and models with a rejection mechanism. We adapt several state-of-the-art tools, and compare them in this challenging setting, showing that no existing work is really robust to realistic assumptions, but overall AlignRE (Li et al., 2024) performs best along all criteria.
Abstract (translated)
零样本关系抽取的目标是利用文本描述的新类型（即之前未见过的）来识别实体提及之间的关系，而不是依赖于有标签的训练示例。以往的工作常常基于一些不切实际的假设：(1) 实体对通常直接编码在输入中，这阻碍了大规模文档数据库查询时的离线预计算；(2) 没有引入拒绝机制，在检索场景下使用这些模型时会偏向于评估，因为在某些情况下（通常是大多数情况），输入是无关的且必须被忽略。在这项工作中，我们研究了现有零样本关系抽取模型在适应现实提取场景时的稳健性。为此，我们介绍了现有模型的分类，并提出了几种策略来构建单次通过模型和具备拒绝机制的模型。我们将几款最先进的工具进行了调整，在这种具有挑战性的设置下进行比较，结果显示目前没有工作能真正应对现实假设的挑战，但总体而言，AlignRE（Li等，2024）在所有标准上表现最佳。
URL
https://arxiv.org/abs/2603.01266
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A Dataset for Named Entity Recognition and Relation Extraction from Art-historical Image Descriptions
2026-02-22 11:29:03Stefanie Schneider, Miriam G\"oldl, Julian Stalter, Ricarda VollmerAbstract
This paper introduces FRAME (Fine-grained Recognition of Art-historical Metadata and Entities), a manually annotated dataset of art-historical image descriptions for Named Entity Recognition (NER) and Relation Extraction (RE). Descriptions were collected from museum catalogs, auction listings, open-access platforms, and scholarly databases, then filtered to ensure that each text focuses on a single artwork and contains explicit statements about its material, composition, or iconography. FRAME provides stand-off annotations in three layers: a metadata layer for object-level properties, a content layer for depicted subjects and motifs, and a co-reference layer linking repeated mentions. Across layers, entity spans are labeled with 37 types and connected by typed RE links between mentions. Entity types are aligned with Wikidata to support Named Entity Linking (NEL) and downstream knowledge-graph construction. The dataset is released as UIMA XMI Common Analysis Structure (CAS) files with accompanying images and bibliographic metadata, and can be used to benchmark and fine-tune NER and RE systems, including zero- and few-shot setups with Large Language Models (LLMs).
Abstract (translated)
本文介绍了FRAME（艺术史元数据和实体的细粒度识别），这是一个由人工注释的艺术史图像描述数据集，用于命名实体识别（NER）和关系抽取（RE）。描述是从博物馆目录、拍卖清单、开放获取平台和学术数据库中收集的，经过筛选以确保每个文本聚焦于单一艺术品，并包含对其材质、构图或象征意义的具体陈述。FRAME提供了三层离散注释：元数据层用于对象级属性；内容层用于描绘的主题和动机；以及共指层连接重复提及的内容。在各层次之间，实体跨度被标记为37种类型并通过提及之间的类型化RE链接进行关联。实体类型与Wikidata对齐以支持命名实体链接（NEL）及下游知识图的构建。该数据集作为UIMA XMI通用分析结构（CAS）文件发布，并附带相关图像和文献元数据，可用于基准测试并微调NER和RE系统，包括大型语言模型（LLMs）的零样本和少样本设置。
URL
https://arxiv.org/abs/2602.19133
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CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
2026-02-19 18:59:44Juri Opitz, Corina Racl\'e, Emanuela Boros, Andrianos Michail, Matteo Romanello, Maud Ehrmann, Simon ClematideAbstract
HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts. Building on the HIPE-2020 and HIPE-2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person--place associations in multiple languages and time periods. Systems are asked to classify relations of two types - $at$ ("Has the person ever been at this place?") and $isAt$ ("Is the person located at this place around publication time?") - requiring reasoning over temporal and geographical cues. The lab introduces a three-fold evaluation profile that jointly assesses accuracy, computational efficiency, and domain generalization. By linking relation extraction to large-scale historical data processing, HIPE-2026 aims to support downstream applications in knowledge-graph construction, historical biography reconstruction, and spatial analysis in digital humanities.
Abstract (translated)
URL
https://arxiv.org/abs/2602.17663
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Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italian
2026-02-19 15:38:46Pietro Ferrazzi, Mattia Franzin, Alberto Lavelli, Bernardo MagniniAbstract
Large Language Models (LLMs) consistently excel in diverse medical Natural Language Processing (NLP) tasks, yet their substantial computational requirements often limit deployment in real-world healthcare settings. In this work, we investigate whether "small" LLMs (around one billion parameters) can effectively perform medical tasks while maintaining competitive accuracy. We evaluate models from three major families-Llama-3, Gemma-3, and Qwen3-across 20 clinical NLP tasks among Named Entity Recognition, Relation Extraction, Case Report Form Filling, Question Answering, and Argument Mining. We systematically compare a range of adaptation strategies, both at inference time (few-shot prompting, constraint decoding) and at training time (supervised fine-tuning, continual pretraining). Fine-tuning emerges as the most effective approach, while the combination of few-shot prompting and constraint decoding offers strong lower-resource alternatives. Our results show that small LLMs can match or even surpass larger baselines, with our best configuration based on Qwen3-1.7B achieving an average score +9.2 points higher than Qwen3-32B. We release a comprehensive collection of all the publicly available Italian medical datasets for NLP tasks, together with our top-performing models. Furthermore, we release an Italian dataset of 126M words from the Emergency Department of an Italian Hospital, and 175M words from various sources that we used for continual pre-training.
Abstract (translated)
URL
https://arxiv.org/abs/2602.17475
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Peeking inside the Black-Box: Reinforcement Learning for Explainable and Accurate Relation Extraction
2026-02-16 14:42:00Xinyu Guo, Zhengliang Shi, Minglai Yang, Mahdi Rahimi, Mihai SurdeanuAbstract
We introduce CogRE, a novel framework for relation extraction (RE), enhancing RE from both accuracy and explainability. The framework has two key components: (i) a reasoning mechanism that formulates relation extraction as a series of text-processing steps inspired by cognitive science, and (ii) an optimization process driven by a novel reinforcement learning (RL) reward function. Our framework introduces relation keywords and rewards generating such keywords using an automatically constructed keywords dictionary. This design addresses the lack of language-based explanations in traditional RE and provides supervision for explanation during RL training. Our experiments show that CogRE improves explanation quality by addressing two common failure patterns in one-shot RE: poor attention focus and limited one-shot learning capability. For example, our cognitive-structured reasoning with Qwen2.5-15B-Instruct on One-shot NYT29 achieves 24.65% F1, surpassing prior reasoning-based designs. Optimizing this approach with RL using our reward further improves performance by +23.46% (absolute). Further, models trained on NYT29 with our reward achieve a +16.9% F1 gain on out-of-distribution WIKIDATA. Finally, human evaluation shows that our best model generates relational keywords closely aligned with gold labels, increasing human explanation quality ratings by 54% (relative).
Abstract (translated)
URL
https://arxiv.org/abs/2510.06198
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RMPL: Relation-aware Multi-task Progressive Learning with Stage-wise Training for Multimedia Event Extraction
2026-02-14 12:43:25Yongkang Jin, Jianwen Luo, Jingjing Wang, Jianmin Yao, Yu HongAbstract
Multimedia Event Extraction (MEE) aims to identify events and their arguments from documents that contain both text and images. It requires grounding event semantics across different modalities. Progress in MEE is limited by the lack of annotated training data. M2E2 is the only established benchmark, but it provides annotations only for evaluation. This makes direct supervised training impractical. Existing methods mainly rely on cross-modal alignment or inference-time prompting with Vision--Language Models (VLMs). These approaches do not explicitly learn structured event representations and often produce weak argument grounding in multimodal settings. To address these limitations, we propose RMPL, a Relation-aware Multi-task Progressive Learning framework for MEE under low-resource conditions. RMPL incorporates heterogeneous supervision from unimodal event extraction and multimedia relation extraction with stage-wise training. The model is first trained with a unified schema to learn shared event-centric representations across modalities. It is then fine-tuned for event mention identification and argument role extraction using mixed textual and visual data. Experiments on the M2E2 benchmark with multiple VLMs show consistent improvements across different modality settings.
Abstract (translated)
URL
https://arxiv.org/abs/2602.13748
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ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images
2026-02-12 17:38:57Mathieu Sibue, Andres Mu\~noz Garza, Samuel Mensah, Pranav Shetty, Zhiqiang Ma, Xiaomo Liu, Manuela VelosoAbstract
Enterprise documents, such as forms and reports, embed critical information for downstream applications like data archiving, automated workflows, and analytics. Although generalist Vision Language Models (VLMs) perform well on established document understanding benchmarks, their ability to conduct holistic, fine-grained structured extraction across diverse document types and flexible schemas is not well studied. Existing Key Entity Extraction (KEE), Relation Extraction (RE), and Visual Question Answering (VQA) datasets are limited by narrow entity ontologies, simple queries, or homogeneous document types, often overlooking the need for adaptable and structured extraction. To address these gaps, we introduce ExStrucTiny, a new benchmark dataset for structured Information Extraction (IE) from document images, unifying aspects of KEE, RE, and VQA. Built through a novel pipeline combining manual and synthetic human-validated samples, ExStrucTiny covers more varied document types and extraction scenarios. We analyze open and closed VLMs on this benchmark, highlighting challenges such as schema adaptation, query under-specification, and answer localization. We hope our work provides a bedrock for improving generalist models for structured IE in documents.
Abstract (translated)
企业文档（如表格和报告）包含下游应用（例如数据归档、自动化工作流和数据分析）所需的至关重要的信息。尽管通用的视觉语言模型（VLMs）在已建立的文档理解基准测试中表现良好，但它们在整个多样化文档类型和灵活模式中的整体细粒度结构化提取能力尚未得到充分研究。现有的关键实体抽取（KEE）、关系抽取（RE）和视觉问答（VQA）数据集受限于狭窄的实体本体、简单的问题或单一类型的文件，往往忽略了对适应性强且具有结构性提取的需求。为了解决这些问题，我们引入了ExStrucTiny，这是一个新的基准数据集，用于从文档图像中进行结构化信息抽取，将KEE、RE和VQA的方面统一起来。通过结合手动和合成的人类验证样本的新颖管道构建而成，ExStrucTiny涵盖了更多种类的文件类型和提取场景。我们在该基准测试上分析了开放和封闭的VLMs，并指出了模式适应性差、问题描述不明确以及答案定位等挑战。我们希望我们的工作为提高文档中的结构化信息抽取的通用模型提供基础。
URL
https://arxiv.org/abs/2602.12203
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FlyAOC: Evaluating Agentic Ontology Curation of Drosophila Scientific Knowledge Bases
2026-02-09 20:12:38Xingjian Zhang, Sophia Moylan, Ziyang Xiong, Qiaozhu Mei, Yichen Luo, Jiaqi W. MaAbstract
Scientific knowledge bases accelerate discovery by curating findings from primary literature into structured, queryable formats for both human researchers and emerging AI systems. Maintaining these resources requires expert curators to search relevant papers, reconcile evidence across documents, and produce ontology-grounded annotations - a workflow that existing benchmarks, focused on isolated subtasks like named entity recognition or relation extraction, do not capture. We present FlyBench to evaluate AI agents on end-to-end agentic ontology curation from scientific literature. Given only a gene symbol, agents must search and read from a corpus of 16,898 full-text papers to produce structured annotations: Gene Ontology terms describing function, expression patterns, and historical synonyms linking decades of nomenclature. The benchmark includes 7,397 expert-curated annotations across 100 genes drawn from FlyBase, the Drosophila (fruit fly) knowledge base. We evaluate four baseline agent architectures: memorization, fixed pipeline, single-agent, and multi-agent. We find that architectural choices significantly impact performance, with multi-agent designs outperforming simpler alternatives, yet scaling backbone models yields diminishing returns. All baselines leave substantial room for improvement. Our analysis surfaces several findings to guide future development; for example, agents primarily use retrieval to confirm parametric knowledge rather than discover new information. We hope FlyBench will drive progress on retrieval-augmented scientific reasoning, a capability with broad applications across scientific domains.
Abstract (translated)
科学知识库通过将原始文献中的发现整理成结构化、可查询的格式，加速了科研领域的发现过程。这些资源不仅供人类研究人员使用，还为新兴的人工智能系统提供支持。然而，维护这类资源需要专家编目人员来搜索相关论文，跨文档比对证据，并生成基于本体论的注释——这是一项现有基准测试无法完全捕捉的工作流程，因为现有的基准测试主要集中在孤立的任务上，如命名实体识别或关系抽取等。 我们提出了FlyBench这一评估AI代理在从科学文献中进行端到端代理式本体编目的方法。给定一个基因符号后，代理必须从包含16,898篇全文论文的语料库中搜索并阅读，以生成结构化的注释：描述功能、表达模式以及连接几十年命名惯例的历史同义词的Gene Ontology术语。该基准测试包含了来自FlyBase（果蝇知识库）的100个基因中的7397项专家编目的注释。 我们评估了四种基础代理架构：记忆型、固定管道型、单一代理型和多代理型。研究发现，架构选择对性能有显著影响，其中多代理设计优于简单的替代方案，但扩展模型主体的规模带来的收益却逐渐减少。所有基准测试的表现都有改进的空间。 我们的分析揭示了一些指导未来发展的关键点；例如，代理主要依靠检索来确认参数知识而不是发现新的信息。我们希望FlyBench能够推动基于检索的科学推理的进步，这一能力在各个科研领域内都有着广泛的应用前景。
URL
https://arxiv.org/abs/2602.09163
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Knowledge Augmented Entity and Relation Extraction for Legal Documents with Hypergraph Neural Network
2026-02-09 05:46:11Binglin Wu, Xianneng LiAbstract
With the continuous progress of digitization in Chinese judicial institutions, a substantial amount of electronic legal document information has been accumulated. To unlock its potential value, entity and relation extraction for legal documents has emerged as a crucial task. However, existing methods often lack domain-specific knowledge and fail to account for the unique characteristics of the judicial domain. In this paper, we propose an entity and relation extraction algorithm based on hypergraph neural network (Legal-KAHRE) for drug-related judgment documents. Firstly, we design a candidate span generator based on neighbor-oriented packing strategy and biaffine mechanism, which identifies spans likely to contain entities. Secondly, we construct a legal dictionary with judicial domain knowledge and integrate it into text encoding representation using multi-head attention. Additionally, we incorporate domain-specific cases like joint crimes and combined punishment for multiple crimes into the hypergraph structure design. Finally, we employ a hypergraph neural network for higher-order inference via message passing. Experimental results on the CAIL2022 information extraction dataset demonstrate that our method significantly outperforms existing baseline models.
Abstract (translated)
随着中国司法机构数字化的不断推进，积累了大量的电子法律文件信息。为了挖掘这些数据的巨大潜力，从法律文档中提取实体和关系成为了一个至关重要的任务。然而，现有的方法通常缺乏特定领域的专业知识，并且未能充分考虑司法领域独有的特点。为此，在本文中我们提出了一种基于超图神经网络（Legal-KAHRE）的药物相关判决文书的实体和关系抽取算法。 首先，我们设计了基于邻居导向打包策略与双线性机制的候选片段生成器，该生成器能够识别可能包含实体的文本片段。其次，我们构建了一个包含司法领域知识的法律词典，并通过多头注意力机制将其集成到文本编码表示中。此外，我们将特定领域的案例如共同犯罪和多项罪行合并处罚纳入超图结构设计之中。 最后，我们利用超图神经网络进行高层次推理，通过消息传递来实现更深层次的理解。在CAIL2022信息抽取数据集上的实验结果显示，我们的方法显著优于现有的基准模型。
URL
https://arxiv.org/abs/2602.08289
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A Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction
2026-02-04 08:38:55Marco Martinelli, Stefano Marchesin, Vanessa Bonato, Giorgio Maria Di Nunzio, Nicola Ferro, Ornella Irrera, Laura Menotti, Federica Vezzani, Gianmaria SilvelloAbstract
Information Extraction (IE), encompassing Named Entity Recognition (NER), Named Entity Linking (NEL), and Relation Extraction (RE), is critical for transforming the rapidly growing volume of scientific publications into structured, actionable knowledge. This need is especially evident in fast-evolving biomedical fields such as the gut-brain axis, where research investigates complex interactions between the gut microbiota and brain-related disorders. Existing biomedical IE benchmarks, however, are often narrow in scope and rely heavily on distantly supervised or automatically generated annotations, limiting their utility for advancing robust IE methods. We introduce GutBrainIE, a benchmark based on more than 1,600 PubMed abstracts, manually annotated by biomedical and terminological experts with fine-grained entities, concept-level links, and relations. While grounded in the gut-brain axis, the benchmark's rich schema, multiple tasks, and combination of highly curated and weakly supervised data make it broadly applicable to the development and evaluation of biomedical IE systems across domains.
Abstract (translated)
信息抽取（IE）包括命名实体识别（NER）、命名实体链接（NEL）和关系提取（RE），对于将快速增长的科学出版物转化为结构化、可操作的知识至关重要。特别是在快速发展的生物医学领域，如肠-脑轴，研究复杂地探讨了肠道微生物群与大脑相关疾病之间的相互作用，这种需求尤为明显。然而，现有的生物医学IE基准测试通常范围狭窄，并且严重依赖于远程监督或自动产生的注释，这限制了它们在推进稳健IE方法方面的实用性。 我们引入了GutBrainIE，这是一个基于超过1,600篇PubMed摘要的基准测试，这些摘要由生物医学和术语学专家手动标注，包含了细粒度实体、概念级链接以及关系。虽然该基准测试建立在肠-脑轴的基础上，但其丰富的架构、多项任务设置及高度策划与弱监督数据的结合，使其能够广泛应用于跨领域生物医学IE系统的开发与评估。
URL
https://arxiv.org/abs/2602.04320
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LEC-KG: An LLM-Embedding Collaborative Framework for Domain-Specific Knowledge Graph Construction -- A Case Study on SDGs
2026-02-02 13:37:17Yikai Zeng, Yingchao Piao, Jianhui LiAbstract
Constructing domain-specific knowledge graphs from unstructured text remains challenging due to heterogeneous entity mentions, long-tail relation distributions, and the absence of standardized schemas. We present LEC-KG, a bidirectional collaborative framework that integrates the semantic understanding of Large Language Models (LLMs) with the structural reasoning of Knowledge Graph Embeddings (KGE). Our approach features three key components: (1) hierarchical coarse-to-fine relation extraction that mitigates long-tail bias, (2) evidence-guided Chain-of-Thought feedback that grounds structural suggestions in source text, and (3) semantic initialization that enables structural validation for unseen entities. The two modules enhance each other iteratively-KGE provides structure-aware feedback to refine LLM extractions, while validated triples progressively improve KGE representations. We evaluate LEC-KG on Chinese Sustainable Development Goal (SDG) reports, demonstrating substantial improvements over LLM baselines, particularly on low-frequency relations. Through iterative refinement, our framework reliably transforms unstructured policy text into validated knowledge graph triples.
Abstract (translated)
URL
https://arxiv.org/abs/2602.02090
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Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning in Few-Shot Relation Extraction
2026-01-28 17:48:58Aunabil Chakma, Mihai Surdeanu, Eduardo BlancoAbstract
This paper presents several strategies to automatically obtain additional examples for in-context learning of one-shot relation extraction. Specifically, we introduce a novel strategy for example selection, in which new examples are selected based on the similarity of their underlying syntactic-semantic structure to the provided one-shot example. We show that this method results in complementary word choices and sentence structures when compared to LLM-generated examples. When these strategies are combined, the resulting hybrid system achieves a more holistic picture of the relations of interest than either method alone. Our framework transfers well across datasets (FS-TACRED and FS-FewRel) and LLM families (Qwen and Gemma). Overall, our hybrid selection method consistently outperforms alternative strategies and achieves state-of-the-art performance on FS-TACRED and strong gains on a customized FewRel subset.
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URL
https://arxiv.org/abs/2601.20803
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DOREMI: Optimizing Long Tail Predictions in Document-Level Relation Extraction
2026-01-16 11:04:18Laura Menotti, Stefano Marchesin, Gianmaria SilvelloAbstract
Document-Level Relation Extraction (DocRE) presents significant challenges due to its reliance on cross-sentence context and the long-tail distribution of relation types, where many relations have scarce training examples. In this work, we introduce DOcument-level Relation Extraction optiMizing the long taIl (DOREMI), an iterative framework that enhances underrepresented relations through minimal yet targeted manual annotations. Unlike previous approaches that rely on large-scale noisy data or heuristic denoising, DOREMI actively selects the most informative examples to improve training efficiency and robustness. DOREMI can be applied to any existing DocRE model and is effective at mitigating long-tail biases, offering a scalable solution to improve generalization on rare relations.
Abstract (translated)
文档级关系抽取（DocRE）面临重大挑战，主要是由于其依赖跨句子的上下文以及关系类型的长尾分布，其中许多关系只有很少的训练样本。在本项工作中，我们引入了DOcument-level Relation Extraction optiMizing the long taIl (DOREMI)，这是一个迭代框架，通过最少但有针对性的手动注释来增强代表性不足的关系。与依赖大规模嘈杂数据或启发式去噪方法的先前方法不同，DOREMI主动选择最有信息量的例子以提高训练效率和鲁棒性。DOREMI可以应用于任何现有的DocRE模型，并且在减轻长尾偏差方面非常有效，提供了一种可扩展的方法来改善罕见关系上的泛化能力。
URL
https://arxiv.org/abs/2601.11190
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Relation Extraction Capabilities of LLMs on Clinical Text: A Bilingual Evaluation for English and Turkish
2026-01-14 10:49:46Aidana Aidynkyzy, O\u{g}uz Dikenelli, Oylum Alatl{\i}, \c{S}ebnem BoraAbstract
The scarcity of annotated datasets for clinical information extraction in non-English languages hinders the evaluation of large language model (LLM)-based methods developed primarily in English. In this study, we present the first comprehensive bilingual evaluation of LLMs for the clinical Relation Extraction (RE) task in both English and Turkish. To facilitate this evaluation, we introduce the first English-Turkish parallel clinical RE dataset, derived and carefully curated from the 2010 i2b2/VA relation classification corpus. We systematically assess a diverse set of prompting strategies, including multiple in-context learning (ICL) and Chain-of-Thought (CoT) approaches, and compare their performance to fine-tuned baselines such as PURE. Furthermore, we propose Relation-Aware Retrieval (RAR), a novel in-context example selection method based on contrastive learning, that is specifically designed to capture both sentence-level and relation-level semantics. Our results show that prompting-based LLM approaches consistently outperform traditional fine-tuned models. Moreover, evaluations for English performed better than their Turkish counterparts across all evaluated LLMs and prompting techniques. Among ICL methods, RAR achieves the highest performance, with Gemini 1.5 Flash reaching a micro-F1 score of 0.906 in English and 0.888 in Turkish. Performance further improves to 0.918 F1 in English when RAR is combined with a structured reasoning prompt using the DeepSeek-V3 model. These findings highlight the importance of high-quality demonstration retrieval and underscore the potential of advanced retrieval and prompting techniques to bridge resource gaps in clinical natural language processing.
Abstract (translated)
在非英语语言中，用于临床信息提取的注释数据集的稀缺性阻碍了主要用英语开发的大规模语言模型（LLM）方法的评估。在这项研究中，我们首次提出了针对英语和土耳其语的临床关系抽取（RE）任务的全面双语评价。为了支持这一评估，我们引入了第一个英土双语文本平行的临床RE数据集，该数据集源自2010年i2b2/VA关系分类语料库，并经过仔细整理。我们系统地评估了一系列不同的提示策略，包括多种上下文学习（ICL）和链式思维（CoT）方法，并将其性能与微调基准模型（如PURE）进行了比较。此外，我们提出了基于对比学习的关系感知检索（RAR），这是一种新型的在上下文示例选择方法，特别设计用于捕捉句子级别和关系级别的语义。我们的结果表明，基于提示的LLM方法始终优于传统的微调模型。而且，在所有评估的LLM和提示技术中，英语的性能都优于土耳其语。在ICL方法中，RAR取得了最高的表现，其中Gemini 1.5 Flash在英语中的微平均F1得分为0.906，在土耳其语中为0.888。当RAR与使用DeepSeek-V3模型的结构化推理提示结合时，性能进一步提高至英语中的0.918 F1得分。这些发现突显了高质量演示检索的重要性，并强调了先进的检索和提示技术在临床自然语言处理资源缺口方面具有巨大的潜力。
URL
https://arxiv.org/abs/2601.09367
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Document-Level Zero-Shot Relation Extraction with Entity Side Information
2026-01-12 07:27:30Mohan Raj Chanthran, Soon Lay Ki, Ong Huey Fang, Bhawani SelvaretnamAbstract
Document-Level Zero-Shot Relation Extraction (DocZSRE) aims to predict unseen relation labels in text documents without prior training on specific relations. Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels, which poses challenges for low-resource languages like Malaysian English. These challenges include the incorporation of local linguistic nuances and the risk of factual inaccuracies in LLM-generated data. This paper introduces Document-Level Zero-Shot Relation Extraction with Entity Side Information (DocZSRE-SI) to address limitations in the existing DocZSRE approach. The DocZSRE-SI framework leverages Entity Side Information, such as Entity Mention Descriptions and Entity Mention Hypernyms, to perform ZSRE without depending on LLM-generated synthetic data. The proposed low-complexity model achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. By utilizing Entity Side Information, DocZSRE-SI offers a robust and efficient alternative to error-prone, LLM-based methods, demonstrating significant advancements in handling low-resource languages and linguistic diversity in relation extraction tasks. This research provides a scalable and reliable solution for ZSRE, particularly in contexts like Malaysian English news articles, where traditional LLM-based approaches fall short.
Abstract (translated)
文档级零样本关系抽取（DocZSRE）的目标是在没有针对特定关系进行前期训练的情况下，预测文本文件中未见过的关系标签。现有的方法依赖于大型语言模型（LLMs）来生成用于未见标签的合成数据，这对于马来西亚英语等资源匮乏的语言来说存在挑战，这些挑战包括难以融入当地的语言细微差别以及由LLM生成的数据中的事实错误风险。 本文提出了一种名为文档级零样本关系抽取带实体侧面信息（DocZSRE-SI）的方法，以解决现有DocZSRE方法的限制。DocZSRE-SI框架利用了诸如实体提及描述和实体提及超类之类的实体侧面信息来进行零样本关系抽取，而无需依赖于LLM生成的合成数据。所提出的低复杂度模型相比基线模型和现有的基准，在宏平均F1分数上提高了11.6%。 通过使用实体侧面信息，DocZSRE-SI为基于LLMs的方法提供了一个稳健且高效的替代方案，表明在处理资源匮乏的语言以及关系抽取任务中的语言多样性方面取得了重大进展。这项研究提供了用于零样本关系抽取的可扩展和可靠解决方案，在马来西亚英语新闻文章等情境下尤其有效，其中传统的基于LLM的方法表现不佳。
URL
https://arxiv.org/abs/2601.07271
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G-MemLLM: Gated Latent Memory Augmentation for Long-Context Reasoning in Large Language Models
2026-01-12 03:30:29Xun XuAbstract
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, yet they remain constrained by the finite capacity of their context windows and the inherent difficulty of maintaining long-term factual consistency during multi-hop reasoning. While existing methods utilize context compression or recurrent tokens, they often suffer from ``context rot'' or the dilution of information over long horizons. In this paper, we propose \textbf{G-MemLLM}, a memory-augmented architecture that integrates a frozen LLM backbone with a trainable \textbf{Latent Memory Bank}. Our key innovation is a GRU-style gated update logic that allows the model to selectively update, preserve, or overwrite latent memory slots, preventing the vanishing gradients of knowledge common in recurrent systems. We evaluate G-MemLLM across scales, from GPT-2 (124M) to Llama 3.1 (8B), on the HotpotQA and Zero-Shot Relation Extraction (ZsRE) benchmarks. Our results demonstrate that G-MemLLM significantly enhances multi-hop reasoning and relational precision, achieving a 13.3\% accuracy boost on ZsRE for Llama 3.1-8B, and it also yields improvements across model scales, boosting Answer F1 by 8.56 points for GPT-2 and increasing Supporting Fact F1 by 6.89 points for Llama 3.1-8B on HotpotQA.
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URL
https://arxiv.org/abs/2602.00015
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Semantic NLP Pipelines for Interoperable Patient Digital Twins from Unstructured EHRs
2026-01-09 15:20:11Rafael Brens, Yuqiao Meng, Luoxi Tang, Zhaohan XiAbstract
Digital twins -- virtual replicas of physical entities -- are gaining traction in healthcare for personalized monitoring, predictive modeling, and clinical decision support. However, generating interoperable patient digital twins from unstructured electronic health records (EHRs) remains challenging due to variability in clinical documentation and lack of standardized mappings. This paper presents a semantic NLP-driven pipeline that transforms free-text EHR notes into FHIR-compliant digital twin representations. The pipeline leverages named entity recognition (NER) to extract clinical concepts, concept normalization to map entities to SNOMED-CT or ICD-10, and relation extraction to capture structured associations between conditions, medications, and observations. Evaluation on MIMIC-IV Clinical Database Demo with validation against MIMIC-IV-on-FHIR reference mappings demonstrates high F1-scores for entity and relation extraction, with improved schema completeness and interoperability compared to baseline methods.
Abstract (translated)
数字孪生——物理实体的虚拟副本——在医疗保健领域用于个性化监测、预测建模和临床决策支持方面正逐渐受到重视。然而，从非结构化的电子健康记录（EHRs）生成可互操作的患者数字孪生仍然面临挑战，主要是由于临床文档的变异性以及缺乏标准化映射所致。本文提出了一种基于语义自然语言处理（NLP）的管道，该管道将自由文本形式的EHR笔记转换为符合FHIR标准的数字孪生表示。此管道利用命名实体识别（NER）来提取临床概念、概念规范化以将实体映射到SNOMED-CT或ICD-10，并通过关系抽取捕捉条件、药物和观察结果之间的结构化关联。 使用MIMIC-IV临床数据库Demo进行评估，对照组为MIMIC-IV-on-FHIR参考映射验证表明，在实体和关系提取方面达到了高F1分数。与基线方法相比，此管道提高了模式完整性和互操作性。
URL
https://arxiv.org/abs/2601.05847
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Clinical Knowledge Graph Construction and Evaluation with Multi-LLMs via Retrieval-Augmented Generation
2026-01-05 07:16:29Udiptaman Das, Krishnasai B. Atmakuri, Duy Ho, Chi Lee, Yugyung LeeAbstract
Large language models (LLMs) offer new opportunities for constructing knowledge graphs (KGs) from unstructured clinical narratives. However, existing approaches often rely on structured inputs and lack robust validation of factual accuracy and semantic consistency, limitations that are especially problematic in oncology. We introduce an end-to-end framework for clinical KG construction and evaluation directly from free text using multi-agent prompting and a schema-constrained Retrieval-Augmented Generation (KG-RAG) strategy. Our pipeline integrates (1) prompt-driven entity, attribute, and relation extraction; (2) entropy-based uncertainty scoring; (3) ontology-aligned RDF/OWL schema generation; and (4) multi-LLM consensus validation for hallucination detection and semantic refinement. Beyond static graph construction, the framework supports continuous refinement and self-supervised evaluation, enabling iterative improvement of graph quality. Applied to two oncology cohorts (PDAC and BRCA), our method produces interpretable, SPARQL-compatible, and clinically grounded knowledge graphs without relying on gold-standard annotations. Experimental results demonstrate consistent gains in precision, relevance, and ontology compliance over baseline methods.
Abstract (translated)
大型语言模型（LLM）为从非结构化临床叙述中构建知识图谱（KG）提供了新的机会。然而，现有的方法通常依赖于结构化的输入，并且缺乏对事实准确性和语义一致性进行稳健验证的能力，在肿瘤学领域尤其成问题。我们引入了一种端到端框架，该框架可以直接从自由文本使用多代理提示和约束模式的检索增强生成（KG-RAG）策略构建并评估临床知识图谱。我们的流水线整合了以下四个关键步骤：(1) 提示驱动下的实体、属性和关系抽取；(2) 基于熵的不确定性评分；(3) 与本体一致的RDF/OWL模式生成；以及 (4) 多LLM共识验证以检测幻觉并进行语义细化。除了静态图谱构建外，该框架还支持持续改进和自我监督评估，从而能够不断优化图的质量。在两个肿瘤学队列（胰腺导管腺癌PDAC 和 BRCA）的应用中，我们的方法无需依赖金标准注释，即可生成可解释、SPARQL兼容且具有临床依据的知识图谱。实验结果表明，在精确度、相关性和符合本体规范方面，相较于基线方法，该方法始终表现出显著的优势。
URL
https://arxiv.org/abs/2601.01844
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