Relation Classification
141 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
Composing Distributed Representations of Relational Patterns
Learning distributed representations for relation instances is a central technique in downstream NLP applications.
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks.
False Positive and Cross-relation Signals in Distant Supervision Data
Distant supervision (DS) is a well-established method for relation extraction from text, based on the assumption that when a knowledge-base contains a relation between a term pair, then sentences that contain that pair are likely to express the relation.
GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification
Georgetown-IR-Lab/semeval2018-task7
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SEMEVAL 2018
SemEval 2018 Task 7 focuses on relation ex- traction and classification in scientific literature.
Adversarial Feature Adaptation for Cross-lingual Relation Classification
zoubowei/feature_adaptation4RC
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COLING 2018
In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language with scarce annotated data.
Word-Level Loss Extensions for Neural Temporal Relation Classification
In this work, we extend our classification model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model.
Crowdsourcing Semantic Label Propagation in Relation Classification
Distant supervision is a popular method for performing relation extraction from text that is known to produce noisy labels.
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
qipeng/gcn-over-pruned-trees
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EMNLP 2018
Dependency trees help relation extraction models capture long-range relations between words.
Using active learning to expand training data for implicit discourse relation recognition
AndreaXu0401/ALIDRC
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EMNLP 2018
We tackle discourse-level relation recognition, a problem of determining semantic relations between text spans.
Revisiting neural relation classification in clinical notes with external information
Recently, segment convolutional neural networks have been proposed for end-to-end relation extraction in the clinical domain, achieving results comparable to or outperforming the approaches with heavy manual feature engineering.
