【論文筆記】Information Extraction over Structured Data: Question Answering with Freebase
一、概要 該文章發於ACL 2014，作者提出了一個基於Freebase knowledge base，結合web-scale語料，通過信息抽取的方法進行KBQA，並在當時取得了比前人方法都要好的效果。 二、模型方法 2.1 任務簡介 假設給一個問題中含有一個或幾個主題（topics），那麼我們就會基於這些主題在知識庫中尋找答案，主題在知識庫中的主題節點（topic node），以及與其相
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相關文章
- 1. Information Extraction over Structured Data: Question Answering with Freebase【論文筆記】
- 2. 【論文筆記】Question Answering over Freebase with Multi-Column Convolutional Neural Networks
- 3. 論文淺嘗 | Question Answering over Freebase
- 4. 【論文筆記】Question Answering with Subgraph Embeddings
- 5. Question Answering with Subgraph Embeddings筆記
- 6. Question Answering with Subgraph Embeddings【論文筆記】
- 7. Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN
- 8. Character-Level Question Answering with Attention 論文筆記
- 9. More Accurate Question Answering on Freebase閱讀筆記
- 10. 論文解讀：Question Answering over Knowledge Base with Neural Attention Combining Global Knowledge Info...