Papers Joint Entity and Relation Extraction
“Joint Entity and Relation Extraction” 태그가 달린 논문 89편 · 필터 해제
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
Joint Entity and Relation Extraction (JERE) is highly sensitive to training data quality, making data augmentation a natural way to improve generalization. However, existing augmentation methods often weaken entity relev…
Joint Entity and Relation ExtractionData AugmentationTIJERE: A Novel Threat Intelligence Joint Extraction Model Based on Analyst Expert Knowledge
The extraction of entities and relationships from threat intelligence reports into structured formats, such as cybersecurity knowledge graphs, is essential for automated threat analysis, detection, and mitigation. Howeve…
Joint Entity and Relation ExtractionKnowledge GraphsDocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data g…
In-Context LearningJoint Entity and Relation ExtractionRelationRelation Extraction+1Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints
This case study applies a phased hyperparameter optimization process to compare multitask natural language model variants that utilize multiphase learning rate scheduling and optimizer parameter grouping. We employ short…
Bayesian OptimizationHyperparameter OptimizationJoint Entity and Relation ExtractionLanguage Modeling+3REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer …
Benchmarkingcoreference-resolutionCoreference ResolutionDocument-level Closed Information Extraction+11EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction
Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs. Despite recent progress, existing approaches often fall short in two key aspects: richne…
Joint Entity and Relation ExtractionKnowledge GraphsRelationRelation ExtractionGraphER: A Structure-aware Text-to-Graph Model for Entity and Relation Extraction
Information extraction (IE) is an important task in Natural Language Processing (NLP), involving the extraction of named entities and their relationships from unstructured text. In this paper, we propose a novel approach…
Graph structure learningJoint Entity and Relation ExtractionRelationRelation Extraction+1An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction
In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem. In contrast to conventional generative information extr…
DecoderJoint Entity and Relation ExtractionRelationRelation ExtractionJoint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most…
Joint Entity and Relation ExtractionNERRelationRelation ExtractionDistantly-Supervised Joint Extraction with Noise-Robust Learning
Joint entity and relation extraction is a process that identifies entity pairs and their relations using a single model. We focus on the problem of joint extraction in distantly-labeled data, whose labels are generated b…
Joint Entity and Relation ExtractionLanguage ModellingLarge Language ModelRelation+1CARE: Co-Attention Network for Joint Entity and Relation Extraction
Joint entity and relation extraction is the fundamental task of information extraction, consisting of two subtasks: named entity recognition and relation extraction. However, most existing joint extraction methods suffer…
Joint Entity and Relation Extractionnamed-entity-recognitionNamed Entity RecognitionRelation+2Similarity-based Memory Enhanced Joint Entity and Relation Extraction
Document-level joint entity and relation extraction is a challenging information extraction problem that requires a unified approach where a single neural network performs four sub-tasks: mention detection, coreference r…
coreference-resolutionCoreference ResolutionJoint Entity and Relation ExtractionMulti-Task Learning+2Enhancing Knowledge Graph Construction Using Large Language Models
The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently. However, the combined application of Large Language Models wit…
graph constructionJoint Entity and Relation ExtractionKnowledge GraphsRelation ExtractionEnd-to-End Temporal Relation Extraction in the Clinical Domain
Temporal relation extraction is an important task in the clinical domain, as it allows a better understanding of the temporal context of clinical events. In this paper, we present an end-to end temporal relation extracti…
Joint Entity and Relation ExtractionRelationRelation ExtractionTemporal Relation ExtractionDocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction. However, most existing works focus on sentence-level coarse-grained JERE, which have limitations in real-world sce…
Joint Entity and Relation ExtractionRelationRelation ClassificationRelation Extraction+190% F1 Score in Relational Triple Extraction: Is it Real ?
Extracting relational triples from text is a crucial task for constructing knowledge bases. Recent advancements in joint entity and relation extraction models have demonstrated remarkable F1 scores ($\ge 90\%$) in accura…
Joint Entity and Relation ExtractionRelationRelation ExtractionVocal Bursts Valence PredictionKnowledge Graph Generation From Text
In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages. The graph nodes are generated first using pretrained lang…
Graph GenerationJoint Entity and Relation ExtractionLanguage ModelingLanguage ModellingQuery-based Instance Discrimination Network for Relational Triple Extraction
Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a…
Contrastive LearningJoint Entity and Relation ExtractionRelationRelation ExtractionSpan-based joint entity and relation extraction augmented with sequence tagging mechanism
Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. However, since previous span-based models rely on span-level classifications, they cannot…
Joint Entity and Relation Extractionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents
We introduce KPI-EDGAR, a novel dataset for Joint Named Entity Recognition and Relation Extraction building on financial reports uploaded to the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, where th…
BenchmarkingJoint Entity and Relation Extractionnamed-entity-recognitionNamed Entity Recognition+4