Relation Classification
140 papers with code • 8 benchmarks • 23 datasets
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
Benchmarks
These leaderboards are used to track progress in Relation Classification
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TACRED
FewRel
FewRel 2.0
Discovery
GUM
LabPics
Subtasks
Most implemented papers
Matching the Blanks: Distributional Similarity for Relation Learning
plkmo/BERT-Relation-Extraction
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ACL 2019
General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction.
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
studio-ousia/luke
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EMNLP 2020
In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
Enriching Pre-trained Language Model with Entity Information for Relation Classification
monologg/R-BERT
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In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.
SpanBERT: Improving Pre-training by Representing and Predicting Spans
facebookresearch/SpanBERT
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TACL 2020
We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
roomylee/entity-aware-relation-classification
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Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.
Advancing NLP with Cognitive Language Processing Signals
Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.
UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction
avramandrei/DeftEval
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SEMEVAL 2020
This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval).
Classifying Relations by Ranking with Convolutional Neural Networks
onehaitao/CR-CNN-relation-extraction
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IJCNLP 2015
Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features.
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
We present a novel end-to-end neural model to extract entities and relations between them.
