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• W2892316911 abstract "Distantly supervised relation extraction employs existing knowledge graphs to automatically collect training data. While distant supervision is effective to scale relation extraction up to large-scale corpora, it inevitably suffers from the wrong labeling problem. Many efforts have been devoted to identifying valid instances from noisy data. However, most existing methods handle each relation in isolation, regardless of rich semantic correlations located in relation hierarchies. In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme. The multiple layers of our hierarchical attention scheme provide coarse-to-fine granularity to better identify valid instances, which is especially effective for extracting those long-tail relations. The experimental results on a large-scale benchmark dataset demonstrate that our models are capable of modeling the hierarchical information of relations and significantly outperform other baselines. The source code of this paper can be obtained from https://github.com/thunlp/HNRE." @default.
• W2892316911 created "2018-09-27" @default.
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• W2892316911 date "2018-01-01" @default.
• W2892316911 modified "2023-10-17" @default.
• W2892316911 title "Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention" @default.
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• W2892316911 doi "https://doi.org/10.18653/v1/d18-1247" @default.
• W2892316911 hasPublicationYear "2018" @default.
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