論文淺嘗-Improving Neural Fine-Grained Entity Typing with Knowledge Attention
這是一篇來自清華2018年的paper。 文章做的是基於知識注意力機制的細粒度實體分類任務（Fine-Grained Entity Typing）。 任務介紹： 給定一個句子，其中包含一個實體和它的上下文，以及一組實體類型，我們的模型旨在預測所提到的實體的每種類型的概率。 **提出問題：**大多數現有的方法通常分別從實體提及和上下文單詞中提取特徵來進行實體類型分類。這些方法不能對實體提及和上下文詞
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- 1. 【論文筆記】【短】Improving Neural Fine-Grained Entity Typing with Knowledge Attention
- 2. 論文學習3-Improving Neural Fine-Grained Entity Typing with Knowledge Attention
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- 7. (2020)Deep Joint Entity Disambiguation with Local Neural Attention論文筆記
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相關文章
- 1. 【論文筆記】【短】Improving Neural Fine-Grained Entity Typing with Knowledge Attention
- 2. 論文學習3-Improving Neural Fine-Grained Entity Typing with Knowledge Attention
- 3. 論文解讀：Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Info...
- 4. 論文淺嘗 | Open world Knowledge Graph Completion
- 5. 論文淺嘗 | K-BERT: Enabling Language Representation with Knowledge Graph
- 6. 論文淺嘗 | Leveraging Knowledge Bases in LSTMs
- 7. (2020)Deep Joint Entity Disambiguation with Local Neural Attention論文筆記
- 8. 論文淺嘗 | Enriching Pre-trained Language Model with Entity Information for Relation Classification
- 9. 論文淺嘗 | CFO: Conditional Focused Neural Question Answering
- 10. 論文解讀：Connecting Embeddings for Knowledge Graph Entity Typing