Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
An Intelligent Multi-task Supply Chain Model Based on Bio-inspired Networks
The sustainability of supply chain plays a key role in achieving optimal performance in controlling the supply chain. The management of risks that occur in a supply chain is a fundamental problem for the purpose of devel…
Relation ClassificationOn the Role of Context for Discourse Relation Classification in Scientific Writing
With the increasing use of generative Artificial Intelligence (AI) methods to support science workflows, we are interested in the use of discourse-level information to find supporting evidence for AI generated scientific…
Relation ClassificationBridging Discourse Treebanks with a Unified Rhetorical Structure Parser
We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and eva…
Relation ClassificationDiscourse ParsingThe Argument is the Explanation: Structured Argumentation for Trust in Agents
Humans are black boxes -- we cannot observe their neural processes, yet society functions by evaluating verifiable arguments. AI explainability should follow this principle: stakeholders need verifiable reasoning chains,…
Relation ClassificationCausal Understanding by LLMs: The Role of Uncertainty
Recent papers show LLMs achieve near-random accuracy in causal relation classification, raising questions about whether such failures arise from limited pretraining exposure or deeper representational gaps. We investigat…
Relation ClassificationCLaC at DISRPT 2025: Hierarchical Adapters for Cross-Framework Multi-lingual Discourse Relation Classification
We present our submission to Task 3 (Discourse Relation Classification) of the DISRPT 2025 shared task. Task 3 introduces a unified set of 17 discourse relation labels across 39 corpora in 16 languages and six discourse …
Relation ClassificationContrastive LearningDeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification
This paper presents DeDisCo, Georgetown University's entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the op…
Relation ClassificationTransformer Enhanced Relation Classification: A Comparative Analysis of Contextuality, Data Efficiency and Sequence Complexity
In the era of large language model, relation extraction (RE) plays an important role in information extraction through the transformation of unstructured raw text into structured data (Wadhwa et al., 2023). In this paper…
Relation ClassificationInformation ExtractionRelation ExtractionART: Adaptive Relation Tuning for Generalized Relation Prediction
Visual relation detection (VRD) is the task of identifying the relationships between objects in a scene. VRD models trained solely on relation detection data struggle to generalize beyond the relations on which they are …
Relation ClassificationDetection of Adverse Drug Events in Dutch clinical free text documents using Transformer Models: benchmark study
In this study, we establish a benchmark for adverse drug event (ADE) detection in Dutch clinical free-text documents using several transformer models, clinical scenarios, and fit-for-purpose performance measures. We trai…
Relation ClassificationError-Aware Curriculum Learning for Biomedical Relation Classification
Relation Classification (RC) in biomedical texts is essential for constructing knowledge graphs and enabling applications such as drug repurposing and clinical decision-making. We propose an error-aware teacher--student …
Relation ClassificationKnowledge GraphsComparative Analysis of AI Agent Architectures for Entity Relationship Classification
Entity relationship classification remains a challenging task in information extraction, especially in scenarios with limited labeled data and complex relational structures. In this study, we conduct a comparative analys…
AI AgentRelationRelation ClassificationRelation ExtractionTowards a More Generalized Approach in Open Relation Extraction
Open Relation Extraction (OpenRE) seeks to identify and extract novel relational facts between named entities from unlabeled data without pre-defined relation schemas. Traditional OpenRE methods typically assume that the…
ClusteringRelationRelation ClassificationRelation ExtractionTowards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting
Memory-based approaches have shown strong performance in Continual Relation Extraction (CRE). However, storing examples from previous tasks increases memory usage and raises privacy concerns. Recently, prompt-based metho…
Continual Relation ExtractionMixture-of-ExpertsRelationRelation Classification+1Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set
Discourse understanding is essential for many NLP tasks, yet most existing work remains constrained by framework-dependent discourse representations. This work investigates whether large language models (LLMs) capture di…
RelationRelation ClassificationHFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
In recent years, action recognition has received much attention and wide application due to its important role in video understanding. Most of the researches on action recognition methods focused on improving the perform…
Action RecognitionRelation ClassificationSkeleton Based Action RecognitionVideo UnderstandingFacial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness
Facial expression captioning has found widespread application across various domains. Recently, the emergence of video Multimodal Large Language Models (MLLMs) has shown promise in general video understanding tasks. Howe…
Event ExtractionInstruction FollowingRelation ClassificationVideo UnderstandingGLiREL -- Generalist Model for Zero-Shot Relation Extraction
We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification. Inspired by recent advancements in zero-shot na…
modelnamed-entity-recognitionNamed Entity RecognitionRelation+3AmalREC: A Dataset for Relation Extraction and Classification Leveraging Amalgamation of Large Language Models
Existing datasets for relation classification and extraction often exhibit limitations such as restricted relation types and domain-specific biases. This work presents a generic framework to generate well-structured sent…
RelationRelation ClassificationRelation ExtractionSentenceAnnotations for Exploring Food Tweets From Multiple Aspects
This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching…
Machine Translationnamed-entity-recognitionNamed Entity RecognitionRelation Classification+1