《Semisupervised Autoencoder for Sentiment Analysis》阅读笔记
(AAAI-16)-Semisupervised Autoencoder for Sentiment Analysis 摘要： 本文研究了自动编码器在文本数据建模中的应用。传统的自动编码器至少有两个方面的问题: 具有较高的词汇量的可扩展性和处理与任务无关的单词。我们通过自动编码器的损失函数引入监督来解决这个问题。特别是, 我们首先在标记的数据上训练一个线性分类器, 然后使用从线性分类器中获得的权重
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相关文章
- 1. 论文阅读笔记：Transfer Learning for Deep Sentiment Analysis
- 2. ACL-2020《KinGDOM：Knowledge-Guided DOMain Adaptation for Sentiment Analysis》阅读笔记
- 3. SentiLR:Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis 论文阅读笔记
- 4. 2017emnlp---Recurrent Attention Network on Memory for Aspect Sentiment Analysis阅读笔记
- 5. Unsupervised Sentiment Analysis with Signed Social Networks--阅读笔记
- 6. 《Review Sentiment Analysis Based on Deep Learning》论文阅读笔记
- 7. Aspect Based Sentiment Analysis with Gated Convolutional Networks 阅读笔记
- 8. acl2018---Aspect Based Sentiment Analysis with Gated Convolutional Networks论文阅读笔记
- 9. 《Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree》阅读笔记
- 10. 《Sentiment Analysis Based on Deep Learning Approaches》阅读笔记