分类:Combining Distant and Direct Supervision for Neural Relation Extraction

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Iz Beltagy, Kyle Lo, Waleed Ammar. Combining Distant and Direct Supervision for Neural Relation Extraction. NAACL-HLT 2019


Abstract

In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model's ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.

总结和评论

这篇文章提出了一个概念关系挖掘的模型,基于直接和间接监督学习的结合。

概念地图

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