2017acl---Linguistically Regularized LSTM for Sentiment Classification笔记
.下面是关于一篇论文的报告 9.报告内容主要来自一篇2017的acl会议的情感分析论文，文章来源的机构是清华大学 10.情感分类是自然语言处理领域的一个经典问题，它往往是将给定的文本根据情感分为positive或negative。 有些场景下，需要对情感进行更加细粒度的分类，例如将positive区分为strong positive和weakly positive。 例如，这三句话中，第一句属于强
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相关文章
- 1. Linguistically Regularized LSTMs for Sentiment Classification
- 2. 《Attention-based LSTM for Aspect-level Sentiment Classification》阅读笔记
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- 4. 《Attention-based LSTM for Aspect-level Sentiment Classification》
- 5. LSTM Networks for Sentiment
- 6. 《Attentional Encoder Network for Targeted Sentiment Classification》
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- 10. 论文阅读笔记：Transformation Networks for Target-Oriented Sentiment Classification