Matching the Blanks: Distributional Similarity for Relation Learning論文筆記
動機 本文主要在於設計一個通用的關係抽取器，即學到一個合適的關係的表達，可以對任意關係建模。之前的工作，如利用Surface Form來表示關係，或者藉助知識庫中關係的定義，但這兩類方案都需要事先定義好關係，泛化能力較弱。 本文受到word2vec中的分佈式假設（Harris’ distributional hypothesis）的啓發，想利用上下文將關係編碼成一個固定長度的向量。這就將關係抽取的
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相關文章
- 1. Matching the Blanks: Distributional Similarity for Relation Learning
- 2. 論文筆記《Matching the Blanks: Distributional Similarity for Relation Learning》
- 3. 【論文筆記】Graph Matching Networks for Learning the Similarity of Graph Structured Objects
- 4. 論文閱讀筆記：Graph Matching Networks for Learning the Similarity of Graph Structured Objects
- 5. 論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》
- 6. Learning to Compare: Relation Network for Few-Shot Learning 論文筆記
- 7. Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction 論文筆記
- 8. 論文閱讀筆記《Power Normalizing Second-order Similarity Network for Few-shot Learning》
- 9. distributional similarity based representations: word2vec
- 10. 論文筆記：Relation Networks for Object Detection