SOTAVerified

Relation Classification

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

Papers

Showing 251–275 of 445 papers

TitleStatusHype
Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision—0
Collocation Classification with Unsupervised Relation VectorsCode0
ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification—0
Joint Type Inference on Entities and Relations via Graph Convolutional Networks—0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data—0
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation ExtractionCode0
Multi-Level Matching and Aggregation Network for Few-Shot Relation ClassificationCode0
Matching the Blanks: Distributional Similarity for Relation LearningCode1
Towards the Data-driven System for Rhetorical Parsing of Russian Texts—0
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN—0
Exploiting Noisy Data in Distant Supervision Relation Classification—0
Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning—0
Enriching Pre-trained Language Model with Entity Information for Relation ClassificationCode0
ERNIE: Enhanced Language Representation with Informative EntitiesCode0
Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting—0
Advancing NLP with Cognitive Language Processing SignalsCode0
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters—0
Mining Discourse Markers for Unsupervised Sentence Representation LearningCode0
A Single Attention-Based Combination of CNN and RNN for Relation Classification—0
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity TypingCode0
End-to-end neural relation extraction using deep biaffine attentionCode0
Customized Attention Mechanism for Relation Classification—0
Improving Scientific Relation Classification with Task Specific Supersense—0
Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification—0
Argumentative Link Prediction using Residual Networks and Multi-Objective LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeepEx (zero-shot top-10)F176.4—Unverified
2DeepStruct multi-taskF174.9—Unverified
3LUKE 483MF172.7—Unverified
4K-AdapterF172—Unverified
5KnowBERTF171.5—Unverified
6MTB Baldini Soares et al. (2019)F171.5—Unverified
7RoBERTaF171.3—Unverified
8SpanBERTF170.8—Unverified
9ERNIEF168—Unverified
10ERNIEF167.97—Unverified
#ModelMetricClaimedVerifiedStatus
1BRCNNF186.3—Unverified
2DRNNsF186.1—Unverified
3depLCNN + NSF185.6—Unverified
4SDP-LSTMF183.7—Unverified
5DepNNF183.6—Unverified
6MVRNNF182.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeepStruct multi-task w/ finetuneF1 (10-way 1-shot)97.8—Unverified
2DeepEx (zero-shot top-10)F192.9—Unverified
3DeepStruct multi-taskF1 (10-way 1-shot)92.2—Unverified
4Deepstruct zero-shotF1 (10-way 1-shot)67.6—Unverified
5DeepEx (zero-shot top-1)F148.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ResAttArgMacro F170.92—Unverified
#ModelMetricClaimedVerifiedStatus
1ResAttArgMacro F142.95—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT1:1 Accuracy20.6—Unverified
#ModelMetricClaimedVerifiedStatus
1ResAttArgMacro F137.72—Unverified
#ModelMetricClaimedVerifiedStatus
1SCS-EEREF10.83—Unverified