Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

@article{Zhang2022GraphAS,
  title={Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification},
  author={Kai Zhang and Qi Liu and Zhenya Huang and Mingyue Cheng and Kunpeng Zhang and Mengdi Zhang and Wei Wu and Enhong Chen},
  journal={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2022}
}
  • Kai Zhang, Qi Liu, Enhong Chen
  • Published 18 May 2022
  • Computer Science
  • Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension… 

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References

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