Papers Relation Prediction
“Relation Prediction” 태그가 달린 논문 187편 · 필터 해제
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning
Panoptic Scene Graph Generation (PSG) integrates instance segmentation with relation understanding to capture pixel-level structural relationships in complex scenes. Although recent approaches leveraging pre-trained visi…
DenoisingGraph GenerationInstance SegmentationPanoptic Scene Graph Generation+4Solving Inequality Proofs with Large Language Models
Inequality proving, crucial across diverse scientific and mathematical fields, tests advanced reasoning skills such as discovering tight bounds and strategic theorem application. This makes it a distinct, demanding front…
Mathematical Problem-SolvingRelation PredictionTowards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method
Argument mining has garnered increasing attention over the years, with the recent advancement of Large Language Models (LLMs) further propelling this trend. However, current argument relations remain relatively simplisti…
Argument MiningRelationRelation PredictionImage-Text Relation Prediction for Multilingual Tweets
Various social networks have been allowing media uploads for over a decade now. Still, it has not always been clear what is their relation with the posted text or even if there is any at all. In this work, we explore how…
Language ModelingLanguage ModellingPredictionRelation+1Rethinking the Role of LLMs for Document-level Relation Extraction: a Refiner with Task Distribution and Probability Fusion
Document-level relation extraction (DocRE) provides a broad context for extracting one or more relations for each entity pair. Large language models (LLMs) have made great progress in relation extraction tasks. However, …
Document-level Relation ExtractionRelationRelation ExtractionRelation PredictionUniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis
Document structure analysis, aka document layout analysis, is crucial for understanding both the physical layout and logical structure of documents, serving information retrieval, document summarization, knowledge extrac…
Document Layout AnalysisDocument SummarizationInformation RetrievalPrediction+3TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs
Fully inductive knowledge graph models can be trained on multiple domains and subsequently perform zero-shot knowledge graph completion (KGC) in new unseen domains. This is an important capability towards the goal of hav…
Knowledge Graph CompletionKnowledge GraphsRelationRelation Prediction+1PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings
Knowledge Graphs (KGs) store human knowledge in the form of entities (nodes) and relations, and are used extensively in various applications. KG embeddings are an effective approach to addressing tasks like knowledge dis…
Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge GraphsLink Prediction+1From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. As networks derived from increasingly large datasets becom…
Graph RegressionHeterogeneous Node ClassificationNode ClassificationRelation PredictionCTINexus: Automatic Cyber Threat Intelligence Knowledge Graph Construction Using Large Language Models
Textual descriptions in cyber threat intelligence (CTI) reports, such as security articles and news, are rich sources of knowledge about cyber threats, crucial for organizations to stay informed about the rapidly evolvin…
ArticlesEntity Alignmentgraph constructionIn-Context Learning+2Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion
Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task in the inductive setting, where at test …
Hyperparameter OptimizationKnowledge Graph CompletionLink PredictionRelation PredictionMUSE: Integrating Multi-Knowledge for Knowledge Graph Completion
Knowledge Graph Completion (KGC) aims to predict the missing [relation] part of (head entity)--[relation]->(tail entity) triplet. Most existing KGC methods focus on single features (e.g., relation types) or sub-graph agg…
Knowledge Graph CompletionRelationRelation PredictionRepresentation Learning+1Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering
While being one of the most popular question types, simple questions such as "Who is the author of Cinderella?", are still not completely solved. Surprisingly, even the most powerful modern Large Language Models are pron…
Entity LinkingGraph Question AnsweringKnowledge Graphsnamed-entity-recognition+5Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences
Relation extraction as an important natural Language processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to ca…
PositionRelationRelation ExtractionRelation Prediction+1ViRED: Prediction of Visual Relations in Engineering Drawings
To accurately understand engineering drawings, it is essential to establish the correspondence between images and their description tables within the drawings. Existing document understanding methods predominantly focus …
Decoderdocument understandingElectrical EngineeringPrediction+2MUSE: Multi-Knowledge Passing on the Edges, Boosting Knowledge Graph Completion
Knowledge Graph Completion (KGC) aims to predict the missing information in the (head entity)-[relation]-(tail entity) triplet. Deep Neural Networks have achieved significant progress in the relation prediction task. How…
Knowledge Graph CompletionRelationRelation PredictionRepresentation Learning+1Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
Inductive relation prediction (IRP) -- where entities can be different during training and inference -- has shown great power for completing evolving knowledge graphs. Existing works mainly focus on using graph neural ne…
Inductive Relation PredictionKnowledge GraphsPredictionRelation+1OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models
Panoptic Scene Graph Generation (PSG) aims to segment objects and recognize their relations, enabling the structured understanding of an image. Previous methods focus on predicting predefined object and relation categori…
Graph Generationobject-detectionObject DetectionPanoptic Scene Graph Generation+5Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing
Structured Sentiment Analysis (SSA) was cast as a problem of bi-lexical dependency graph parsing by prior studies. Multiple formulations have been proposed to construct the graph, which share several intrinsic drawbacks:…
Dependency Parsingglobal-optimizationRelation PredictionSentiment AnalysisTowards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing
Cross-document Relation Extraction aims to predict the relation between target entities located in different documents. In this regard, the dominant models commonly retain useful information for relation prediction via b…
PredictionRelationRelation ExtractionRelation Prediction