Search Results for author: Shengda Fan

Found 2 papers, 1 papers with code

CETA: A Consensus Enhanced Training Approach for Denoising in Distantly Supervised Relation Extraction

1 code implementation COLING 2022 Ruri Liu, Shasha Mo, Jianwei Niu, Shengda Fan

This paper proposes a sentence-level DSRE method beyond typical instance selection approaches by preventing samples from falling into the wrong classification space on the feature space.

Classification Denoising +2

Key Mention Pairs Guided Document-Level Relation Extraction

no code implementations COLING 2022 Feng Jiang, Jianwei Niu, Shasha Mo, Shengda Fan

To this end, we propose a novel DocRE model called Key Mention pairs Guided Relation Extractor (KMGRE) to directly model mention-level relations, containing two modules: a mention-level relation extractor and a key instance classifier.

Document-level Relation Extraction

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