Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (AAAI-18) 原文下載 研究方向 情感分析 研究目的 分析人們對於特定屬性的情感傾向是自然語言理解中的一項重要任務。在本文中，提出了一種對於實體屬性情感分析的新穎的方法，通過加入常識知識來進行屬
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相關文章
- 1. 《Attentional Encoder Network for Targeted Sentiment Classification》
- 2. 《Knowledge-enriched Two-layered Attention Network for Sentiment Analysis》
- 3. 【EMNLP2019】Event Representation Learning Enhanced with External Commonsense Knowledge
- 4. Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
- 5. LSTM Networks for Sentiment
- 6. [論文] Aspect Based Sentiment Analysis with Gated Convolutional Networks
- 7. SentiLR:Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis 論文閱讀筆記
- 8. Sentiment Analysis resources
- 9. 《Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree》閱讀筆記
- 10. (ACL2019)Open-DomainTargeted Sentiment Analysis via Span-Based Extraction and Classification