论文浅尝-Improving Neural Fine-Grained Entity Typing with Knowledge Attention
这是一篇来自清华2018年的paper。 文章做的是基于知识注意力机制的细粒度实体分类任务（Fine-Grained Entity Typing）。 任务介绍： 给定一个句子，其中包含一个实体和它的上下文，以及一组实体类型，我们的模型旨在预测所提到的实体的每种类型的概率。 **提出问题：**大多数现有的方法通常分别从实体提及和上下文单词中提取特征来进行实体类型分类。这些方法不能对实体提及和上下文词
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相关文章
- 1. 【论文笔记】【短】Improving Neural Fine-Grained Entity Typing with Knowledge Attention
- 2. 论文学习3-Improving Neural Fine-Grained Entity Typing with Knowledge Attention
- 3. 论文解读：Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Info...
- 4. 论文浅尝 | Open world Knowledge Graph Completion
- 5. 论文浅尝 | K-BERT: Enabling Language Representation with Knowledge Graph
- 6. 论文浅尝 | Leveraging Knowledge Bases in LSTMs
- 7. (2020)Deep Joint Entity Disambiguation with Local Neural Attention论文笔记
- 8. 论文浅尝 | Enriching Pre-trained Language Model with Entity Information for Relation Classification
- 9. 论文浅尝 | CFO: Conditional Focused Neural Question Answering
- 10. 论文解读：Connecting Embeddings for Knowledge Graph Entity Typing