Summary of An Innovative Cgl-mha Model For Sarcasm Sentiment Recognition Using the Mindspore Framework, by Zhenkai Qin and Qining Luo and Xunyi Nong
An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework
by Zhenkai Qin, Qining Luo, Xunyi Nong
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This innovative research paper proposes a novel approach to detecting sarcastic expressions in user-generated content, specifically addressing challenges posed by the widespread use of the Internet and social media. The proposed model integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Multi-Head Attention mechanisms. This hybrid approach enables the capture of local n-gram features, sequential dependencies, and contextual information, while also enhancing the focus on relevant parts of the input through Multi-Head Attention. Experimental results demonstrate that the model achieves high accuracy and F1 scores on two datasets, Headlines (81.20%) and Riloff (79.72%), outperforming traditional models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper finds a new way to detect sarcasm in messages people share online. Sarcasm can be tricky because it often uses happy or exaggerated words to express sadness or anger. The researchers created a special computer model that combines different techniques to help computers understand when someone is being sarcastic. They tested their model on two big collections of social media posts and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Cnn » Lstm » Multi head attention » N gram
