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
Latest papers with no code
How to Encode Domain Information in Relation Classification
Current language models require a lot of training data to obtain high performance.
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
Human interventions to the rules for the TACRED relation \texttt{org:parents} boost the performance on that relation by as much as 26\% relative improvement, without negatively impacting the other relations, and without retraining the semantic matching component.
Prompting Implicit Discourse Relation Annotation
Pre-trained large language models, such as ChatGPT, archive outstanding performance in various reasoning tasks without supervised training and were found to have outperformed crowdsourcing workers.
Noise in Relation Classification Dataset TACRED: Characterization and Reduction
Towards the first objective, we analyze predictions and performance of state-of-the-art (SOTA) models to identify the root cause of noise in the dataset.
Improving Vision-and-Language Reasoning via Spatial Relations Modeling
Further, we design two pre-training tasks named object position regression (OPR) and spatial relation classification (SRC) to learn to reconstruct the spatial relation graph respectively.
Discourse Relations Classification and Cross-Framework Discourse Relation Classification Through the Lens of Cognitive Dimensions: An Empirical Investigation
Our experiments on cross-framework discourse relation classification (PDTB & RST) demonstrate that it is possible to transfer knowledge of discourse relations for one framework to another framework by means of these dimensions, in spite of differences in discourse segmentation of the two frameworks.
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i. e. event, time, and document creation time (DCT).
A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods
Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format.
Rethinking Relation Classification with Graph Meaning Representations
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research.
PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Relation extraction aims to classify the relationships between two entities into pre-defined categories.
