Papers Document-level Relation Extraction
“Document-level Relation Extraction” 태그가 달린 논문 112편 · 필터 해제
Ontology-Driven Structural Regularization for Document-Level Relation Extraction
Document-Level Relation Extraction (DocRE) relies heavily on costly manually annotated datasets, while large distant supervision resources such as DocRED distant remain underexploited due to noise. We show that a critica…
Document-level Relation ExtractionA Domain-Specific Curated Benchmark for Entity and Document-Level Relation Extraction
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 …
Document-level Relation ExtractionInformation ExtractionEntity LinkingDOREMI: Optimizing Long Tail Predictions in Document-Level Relation Extraction
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 example…
Document-level Relation ExtractionRelation as a Prior: A Novel Paradigm for LLM-based Document-level Relation Extraction
Large Language Models (LLMs) have demonstrated their remarkable capabilities in document understanding. However, recent research reveals that LLMs still exhibit performance gaps in Document-level Relation Extraction (Doc…
Document-level Relation ExtractionGLiDRE: Generalist Lightweight model for Document-level Relation Extraction
Relation Extraction (RE) is a fundamental task in Natural Language Processing, and its document-level variant poses significant challenges, due to complex interactions between entities across sentences. While supervised …
Document-level Relation ExtractionMulti-Relation Extraction in Entity Pairs using Global Context
In document-level relation extraction, entities may appear multiple times in a document, and their relationships can shift from one context to another. Accurate prediction of the relationship between two entities across …
Document-level Relation ExtractionRethinking 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 PredictionCOMM:Concentrated Margin Maximization for Robust Document-Level Relation Extraction
Document-level relation extraction (DocRE) is the process of identifying and extracting relations between entities that span multiple sentences within a document. Due to its realistic settings, DocRE has garnered increas…
Document-level Relation ExtractionRelationRelation ExtractionEnhancing Biomedical Relation Extraction with Directionality
Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemicals. The vast growth of biomedical litera…
BenchmarkingDocument-level Relation ExtractionLanguage ModelingLanguage Modelling+4KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities
Document-level relation extraction (Doc-RE) aims to extract relations between entities across multiple sentences. Therefore, Doc-RE requires more comprehensive reasoning abilities like humans, involving complex cross-sen…
Common Sense ReasoningDocument-level Relation ExtractionGeneral KnowledgeLogical Reasoning+4VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction
Document-level Relation Extraction (DocRE) aims to identify relationships between entity pairs within a document. However, most existing methods assume a uniform label distribution, resulting in suboptimal performance on…
Data AugmentationDocument-level Relation ExtractionRelation+1Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning
Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning capability on many NLP tasks. Recasting an NLP task into a text-to-text generation task is a common practice so t…
Document-level Relation ExtractionFew-Shot LearningRelationRelation Extraction+2TPN: Transferable Proto-Learning Network towards Few-shot Document-Level Relation Extraction
Few-shot document-level relation extraction suffers from poor performance due to the challenging cross-domain transferability of NOTA (none-of-the-above) relation representation. In this paper, we introduce a Transferabl…
Document-level Relation ExtractionRelationRelation ExtractionDiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction
The remarkable capabilities of Large Language Models (LLMs) in text comprehension and generation have revolutionized Information Extraction (IE). One such advancement is in Document-level Relation Triplet Extraction (Doc…
Document-level Relation ExtractionReading ComprehensionRelationRelation Extraction+2LLM with Relation Classifier for Document-Level Relation Extraction
Large language models (LLMs) have created a new paradigm for natural language processing. Despite their advancement, LLM-based methods still lag behind traditional approaches in document-level relation extraction (DocRE)…
Document-level Relation ExtractionRelationRelation ClassificationRelation ExtractionGEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction
Document-level relation extraction (DocRE) aims to extract relations between entities from unstructured document text. Compared to sentence-level relation extraction, it requires more complex semantic understanding from …
Document-level Relation ExtractionRelationRelation ExtractionRetrieval+1Consistent Document-Level Relation Extraction via Counterfactuals
Many datasets have been developed to train and evaluate document-level relation extraction (RE) models. Most of these are constructed using real-world data. It has been shown that RE models trained on real-world data suf…
counterfactualDocument-level Relation ExtractionRelationRelation ExtractionEVA-Score: Evaluating Abstractive Long-form Summarization on Informativeness through Extraction and Validation
Since LLMs emerged, more attention has been paid to abstractive long-form summarization, where longer input sequences indicate more information contained. Nevertheless, the automatic evaluation of such summaries remains …
Document-level Relation ExtractionFormInformativenessRelation ExtractionAugmenting Document-level Relation Extraction with Efficient Multi-Supervision
Despite its popularity in sentence-level relation extraction, distantly supervised data is rarely utilized by existing work in document-level relation extraction due to its noisy nature and low information density. Among…
Document-level Relation ExtractionRelationRelation ExtractionSentenceCombining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose …
Document-level Relation Extractionimage-classificationImage ClassificationMulti-Label Classification+4