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Relation Classification
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Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.
Source: Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification
Relation Transformer Network
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
A Neural Approach to Discourse Relation Signal Detection
Learning to Relate from Captions and Bounding Boxes
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
Type-aware Convolutional Neural Networks for Slot Filling
Argumentative Relation Classification as Plausibility Ranking
Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Knowledge Enhanced Contextual Word Representations
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text
Few-shot Text Classification with Distributional Signatures
Zero-shot transfer for implicit discourse relation classification
SpanBERT: Improving Pre-training by Representing and Predicting Spans
Implicit Discourse Relation Identification for Open-domain Dialogues
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
Matching the Blanks: Distributional Similarity for Relation Learning
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning
Enriching Pre-trained Language Model with Entity Information for Relation Classification
ERNIE: Enhanced Language Representation with Informative Entities
Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting
Advancing NLP with Cognitive Language Processing Signals
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters
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