(ACL2019)Open-DomainTargeted Sentiment Analysis via Span-Based Extraction and Classification
open-domain targeted任务主要分两部分，第一步是把句子中的target检测出来，第二步是对target进行情感分类。 文章提出了一种基于新的数据标注方式的方法，解决了传统tag标注的搜索空间大且同一target下的不同单词可能被归入不同情感标签的缺点，以bert为基础模型进行了实验。 论文提出的Span-Based标注方法如下： 下图表示的是论文整个模型的工作过程，提取出targ
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相关文章
- 1. Sentiment Analysis resources
- 2. course1-3_supervised-ml-sentiment-analysis
- 3. Lab1:Sentiment Analysis（dictionary method）
- 4. Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
- 5. 《Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree》阅读笔记
- 6. Sparse Principal Component Analysis via Rotation and Truncation
- 7. Joint Event Extraction via Recurrent Neural Networks
- 8. 《A Joint Model of Term Exaction and Polarity Classification for Aspect-based Sentiment Analysis》阅读笔记
- 9. SemEval2019Task3_ERC | (3) Using Deep Sentiment Analysis Models and Transfer Learning for ERC
- 10. 论文阅读：Leveraging multimodal information for event summarization and concept-level sentiment analysis