[EMNLP2015]Distant supervision for Relation Extraction via Piecewise Convolutional Neural Networks

时间 2021-01-02

标签 PCNN 繁體版

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paper中提高两个概念： (1)distant supervision: 如果两个实体在已知知识库中有关系，则包含这两个实体的中，两个实体的的关系默认就是知识库中给定的关系, 因为人工标注语料代价大，所以一般用distant supervision标注语料 (2)multi-instance learning:训练语料是很多带有正负标签的包，每个包里有很多示例，如果一个包里包含至少一个示例是正标

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1. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
2. [论文研读]Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
3. 论文笔记：Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
4. NLP——Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
5. 【论文阅读笔记PCNN】Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
6. nlp论文学习（关系抽取）——Distant Supervision for Relation Extraction via Piecewise Convolutional Neural （2015）
7. [ACL2016]Neural Relation Extraction with Selective Attention over Instances
8. 【Part two: Related Work】Relation Extraction with Distant Supervision(DS)
9. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
10. 《GAN Driven Semi-distant Supervision for Relation Extraction》解读
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1. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
2. [论文研读]Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
3. 论文笔记：Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
4. NLP——Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
5. 【论文阅读笔记PCNN】Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
6. nlp论文学习（关系抽取）——Distant Supervision for Relation Extraction via Piecewise Convolutional Neural （2015）
7. [ACL2016]Neural Relation Extraction with Selective Attention over Instances
8. 【Part two: Related Work】Relation Extraction with Distant Supervision(DS)
9. Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
10. 《GAN Driven Semi-distant Supervision for Relation Extraction》解读

>>更多相关文章<<