《Semisupervised Autoencoder for Sentiment Analysis》閱讀筆記
(AAAI-16)-Semisupervised Autoencoder for Sentiment Analysis 摘要： 本文研究了自動編碼器在文本數據建模中的應用。傳統的自動編碼器至少有兩個方面的問題: 具有較高的詞彙量的可擴展性和處理與任務無關的單詞。我們通過自動編碼器的損失函數引入監督來解決這個問題。特別是, 我們首先在標記的數據上訓練一個線性分類器, 然後使用從線性分類器中獲得的權重
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相關文章
- 1. 論文閱讀筆記：Transfer Learning for Deep Sentiment Analysis
- 2. ACL-2020《KinGDOM：Knowledge-Guided DOMain Adaptation for Sentiment Analysis》閱讀筆記
- 3. SentiLR:Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis 論文閱讀筆記
- 4. 2017emnlp---Recurrent Attention Network on Memory for Aspect Sentiment Analysis閱讀筆記
- 5. Unsupervised Sentiment Analysis with Signed Social Networks--閱讀筆記
- 6. 《Review Sentiment Analysis Based on Deep Learning》論文閱讀筆記
- 7. Aspect Based Sentiment Analysis with Gated Convolutional Networks 閱讀筆記
- 8. acl2018---Aspect Based Sentiment Analysis with Gated Convolutional Networks論文閱讀筆記
- 9. 《Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree》閱讀筆記
- 10. 《Sentiment Analysis Based on Deep Learning Approaches》閱讀筆記