[EMNLP2017]Context-Aware Representations for Knowledge Base Relation Extraction(short paper))

时间 2021-01-07

标签 relation 繁體版

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在上面给出的例子（１）中，任务是判断e1和e2的关系，在进行判断的时候，借助了e1和e3的关系. e1和e3的关系是在判断e1和e2之前已经识别出来．那么又引出了一个问题：如何表示e1和e3的关系？图１给出的是实体e1和e2实体之间的关系判定，由下至上看此图，输入有两个序列(1)句子序列 x1 , x2 , …, xn (2)实体标签序列：标签B- ∗ , I- ∗ , L- ∗ , O, U-

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1. Context-Aware Representations for Knowledge Base Relation Extraction论文笔记
2. Improved Neural Relation Detection for Knowledge Base Question Answering
3. 论文解读：Improved Neural Relation Detection for Knowledge Base Question Answering
4. Paper Reading《Relation Networks for Object Detection》
5. Learning over Knowledge-Base Embeddings for Recommendation 论文
6. 一、 Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
7. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering笔记
8. 【论文笔记】Enhancing Pre-Trained Language Representations with Rich Knowledge for MRC
9. Paper--IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation
10. Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings

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