【譯文】 The Author-Topic Model for Authors and Documents ( Michal Rosen-Zvi, Thomas Griffiths, etc. )
Abstract 咱們提出了author-topic model，一個利用LDA將做者信息也包含進來的文檔生成模型。每個做者都與主題的多項式分佈有關聯，每個主題都與詞的多項式分佈相關聯。一份包含多個做者的文檔被建模成一個在主題上關於該文檔做者的混合分佈。咱們將該模型應用到一個包含1700篇NIPS會議論文與160000篇CiteSeer文獻摘要的數據集中。精確地推理對於這些數據集是很是困難的，因此
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相關文章
- 1. A Hierarchical Neural Autoencoder for Paragraphs and Documents
- 2. Lucene: Boosting documents and fields
- 3. 【CTR預估】The Wide and Deep Learning Model（譯文+Tensorlfow源碼解析）
- 4. 【翻譯】New Lane Model and Distance Transform for Lane Detection and Tracking
- 5. POJ-2018 Authors Register Update your info Authors ranklist
- 6. SysML Models and Model Transformation for Security
- 7. Model-Based Reinforcement Learning: Theory and Practice 譯文
- 8. Distributed Representations of Sentences and Documents
- 9. 【論文翻譯】EIDETIC 3D LSTM: A MODEL FOR VIDEO PREDICTION AND BEYOND
- 10. The Dataflow Model 論文