Papers with Code 아카이브
Relation Extraction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 15
- 평가표
- 51
- 보존된 평가 기록
- 373
Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.
Source: Deep Residual Learning for Weakly-Supervised Relation Extraction
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
The Joint Entity-Relation Extraction Model Based on Span and Interactive Fusion Representation for Chinese Medical Texts with Complex Semantics
KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
MAQInstruct: Instruction-based Unified Event Relation Extraction
Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution
Enhancing Biomedical Relation Extraction with Directionality
HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
Facial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness
Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier
Towards a scalable AI-driven framework for data-independent Cyber Threat Intelligence Information Extraction
GLiREL -- Generalist Model for Zero-Shot Relation Extraction
CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction
Rethinking Relation Extraction: Beyond Shortcuts to Generalization with a Debiased Benchmark
KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities
AmalREC: A Dataset for Relation Extraction and Classification Leveraging Amalgamation of Large Language Models
KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis
DragonVerseQA: Open-Domain Long-Form Context-Aware Question-Answering
VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation Extraction
Can AI Extract Antecedent Factors of Human Trust in AI? An Application of Information Extraction for Scientific Literature in Behavioural and Computer Sciences
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models
Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature
LLMs-in-the-Loop Part 2: Expert Small AI Models for Anonymization and De-identification of PHI Across Multiple Languages
AI-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
Enhancing Relation Extraction via Supervised Rationale Verification and Feedback
Annotations for Exploring Food Tweets From Multiple Aspects
Diversity Over Quantity: A Lesson From Few Shot Relation Classification
M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction
Prompting Large Language Models for Clinical Temporal Relation Extraction
A survey on cutting-edge relation extraction techniques based on language models
RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements
LLM-IE: A Python Package for Generative Information Extraction with Large Language Models
Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?
Assessing Open-Source Large Language Models on Argumentation Mining Subtasks
Beyond the Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboards
A Library Perspective on Supervised Text Processing in Digital Libraries: An Investigation in the Biomedical Domain
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.