圖計算論文筆記--Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding 已有的方法在學習後得到實體的類別有很多，但有的不需要的噪音。 如圖，對特朗普的定位是person，politician，businessman，artist，actor。但在不同的語境下，其實並不能把特朗普打上這些所有的標籤，其實只是其中的
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相關文章
- 1. 【論文筆記】Embedding of Embedding (EOE) : Joint Embedding for Coupled Heterogeneous Networks
- 2. 【論文筆記】Noise Mitigation for Neural Entity Typing and Relation Extraction
- 3. 【論文筆記】【短】Noise Mitigation for Neural Entity Typing and Relation Extraction
- 4. 計算機視覺論文筆記五：Maximal Linear Embedding for Dimensionality Reduction
- 5. 【論文筆記】Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems
- 6. 論文筆記：Nonlinear Dimensionality Reduction by Locally Linear Embedding
- 7. 【論文筆記】NRL-Heterogeneous Information Network
- 8. Nonlinear Dimensionality Reduction by Locally Linear Embedding
- 9. 論文閱讀筆記－ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering
- 10. [論文筆記]EMNLP2019: Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks