attention+RNN做文本情感分類《Recurrent Attention Network on Memory for Aspect Sentiment Analysis》
原文鏈接 本文發表於自然語言處理領域頂級會議 ACL 2017 代碼鏈接 摘要 本文提出了一種基於多重attention的可以捕捉長距離情感特徵的框架，該框架對無關信息具有更強的魯棒性，並且將多重attention的結果與RNN進行非線性組合，從而模型能夠提取更加複雜的特徵。實驗表明本文提出的框架效果不錯。 模型 我們假設輸入句子是一個序列 s={s1,s2,...,sτ−1,sτ,sτ+1,..
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相關文章
- 1. 2017emnlp---Recurrent Attention Network on Memory for Aspect Sentiment Analysis閱讀筆記
- 2. Recurrent Attention Network on Memory for Aspect Sentiment 閱讀筆記
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- 4. Aspect Level Sentiment Classification with Deep Memory Network筆記
- 5. 方面級paper8Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis(2019ACL)
- 6. [論文] Aspect Based Sentiment Analysis with Gated Convolutional Networks
- 7. [論文]Attention-based LSTM for Aspect-level Sentiment Classificatio
- 8. 《Knowledge-enriched Two-layered Attention Network for Sentiment Analysis》
- 9. Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Sentiment Clas
- 10. 《Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis》學習筆記