論文筆記（NLP）——Sentiment Classification using Document Embeddings trained with Cosine Similarity
1. Abstract Document embedding將每個文檔映射到連續向量空間中一個密集的低維的向量。本文提出了使用餘弦相似度（cosine similarity）代替點積（dot product）來訓練document embedding。 數據集：IMDB 實驗表明： 與dot product相比，使用cosine similarity可以提高準確性； 將naive bayes的n-
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相關文章
- 1. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 論文閱讀筆記
- 2. 【論文閱讀筆記】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 3. Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages論文筆記
- 4. Aspect Level Sentiment Classification with Deep Memory Network論文學習筆記
- 5. Enriching BERT with Knowledge Graph Embeddings for Document Classification
- 6. Sentiment Classification towards Question-Answering with Hierarchical Matching Network 論文閱讀筆記
- 7. Aspect Level Sentiment Classification with Deep Memory Network筆記
- 8. Question Answering with Subgraph Embeddings【論文筆記】
- 9. 【論文筆記】Question Answering with Subgraph Embeddings
- 10. 閱讀分享：Enriching BERT with Knowledge Graph Embeddings for Document Classification