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Mar 22, 2021 · This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in ...
Jan 12, 2021 · This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings.
Relation extraction aims to predict relations between entities in sentences, which is crucial for under- standing the structure of human knowledge and ...
This repo contains the code of the pretraining method proposed in Prototypical Representation Learning for Relation Extraction
This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings.
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In this paper, we propose an effective and parameter-less Prototype Rectification Method (PRM) to promote few-shot relation extraction.
In this paper, we propose a novel method based on Instance Prompting and Prototype Rectification (IPPR) to conduct relational representation learning for ...
In this paper, we propose an effective and parameter-less Prototype Rectification Method (PRM) to promote few-shot relation extraction.
Feb 28, 2024 · To address the problem of imbalanced data, we propose the Contrastive Prototypical Temporal Relational Extraction (CPTRE) model, as illustrated ...
Our proposed ConPL is mainly composed of three modules: 1) a prototype-based classification module that provides primary relation predictions under few-shot ...