跨语言的深层情感：多语言 WordNet 中情感传播的新方法
Deep Emotions Across Languages: A Novel Approach for Sentiment Propagation in Multilingual WordNets
AI总结：
针对 WordNet 情感元数据人工标注不足的问题，提出 MSSE 与 CLDNS 实现词网内及跨语言情感自动传播，并在 Princeton WordNet 和 Polish WordNet 上验证其优于现有方法。
摘要：
情感分析会使用富含情感元数据的 WordNet，而这些都是宝贵资源。然而，人工标注耗时且成本高昂，导致只有少数 WordNet 词汇单元得到标注。本文介绍了两项新技术，用于将情感标注从部分标注的 WordNet 自动传播到其整体以及另一种语言的 WordNet：多语言结构化同义词集嵌入（Multilingual Structured Synset Embeddings，MSSE）和跨语言深度神经情感传播（Cross-Lingual Deep Neural Sentiment Propagation，CLDNS）。我们利用具有大量语际关系的 Princeton WordNet 和 Polish WordNet，对所提出的 MSSE+CLDNS 方法进行了广泛评估。结果表明，MSSE+CLDNS 方法优于现有传播方法，表明其在跨多种语言用情感元数据丰富 WordNet 方面的有效性。本工作为大规模多语言情感分析奠定了坚实基础，对学术研究和实际应用均具有价值。
英文摘要：
Sentiment analysis involves using WordNets enriched with emotional metadata, which are valuable resources. However, manual annotation is time-consuming and expensive, resulting in only a few WordNet Lexical Units being annotated. This paper introduces two new techniques for automatically propagating sentiment annotations from a partially annotated WordNet to its entirety and to a WordNet in a different language: Multilingual Structured Synset Embeddings (MSSE) and Cross-Lingual Deep Neural Sentiment Propagation (CLDNS). We evaluated the proposed MSSE+CLDNS method extensively using Princeton WordNet and Polish WordNet, which have many inter-lingual relations. Our results show that the MSSE+CLDNS method outperforms existing propagation methods, indicating its effectiveness in enriching WordNets with emotional metadata across multiple languages. This work provides a solid foundation for large-scale, multilingual sentiment analysis and is valuable for academic research and practical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
