DESS：基于DeBERTa增强的句法-语义方面情感三元组提取
DESS: DeBERTa Enhanced Syntactic-Semantic Aspect Sentiment Triplet Extraction
AI总结：
针对方面情感三元组提取（ASTE）中准确捕捉方面、观点与情感极性关系的挑战，提出DESS方法，整合DeBERTa增强注意力机制与LSTM双通道处理语义和语法模式，在标准数据集上F1分数显著提升，有效提升细粒度情感分析性能。
摘要：
细粒度情感分析在方面情感三元组提取（ASTE）中面临持续挑战，尤其是在准确捕捉方面、观点和情感极性之间的关系方面。尽管研究人员使用BERT和图神经网络取得了进展，但高级语言模型在理解复杂语言模式方面的全部潜力仍未被探索。我们提出DESS，一种基于先前工作的新方法，通过整合DeBERTa的增强注意力机制以更好地理解文本中的上下文和关系。我们的框架保持双通道结构，其中DeBERTa与LSTM通道协同工作，处理文本中的意义和语法模式。我们仔细优化了这些组件的协作方式，特别关注不同类型语言信息的交互。在标准数据集上测试DESS时，它在识别方面-观点对和准确确定情感方面显示出对当前方法的显著改进，F1分数分别提高了4.85、8.36和2.42。深入研究结果发现，DeBERTa复杂的注意力系统帮助DESS更好地处理复杂句子结构，尤其是在重要单词相距较远时。我们的研究结果表明，经过深思熟虑的整合，升级到更先进的语言模型可以切实改善文本情感分析的性能。我们的方法实现已公开：https://github.com/VishalRepos/DESS。
英文摘要：
Fine-grained sentiment analysis faces ongoing challenges in Aspect Sentiment Triple Extraction (ASTE), particularly in accurately capturing the relationships between aspects, opinions, and sentiment polarities. While researchers have made progress using BERT and Graph Neural Networks, the full potential of advanced language models in understanding complex language patterns remains unexplored. We introduce DESS, a new approach that builds upon previous work by integrating DeBERTa's enhanced attention mechanism to better understand context and relationships in text. Our framework maintains a dual-channel structure, where DeBERTa works alongside an LSTM channel to process both meaning and grammatical patterns in text. We have carefully refined how these components work together, paying special attention to how different types of language information interact. When we tested DESS on standard datasets, it showed meaningful improvements over current methods, with F1-score increases of 4.85, 8.36, and 2.42 in identifying aspect opinion pairs and determining sentiment accurately. Looking deeper into the results, we found that DeBERTa's sophisticated attention system helps DESS handle complicated sentence structures better, especially when important words are far apart. Our findings suggest that upgrading to more advanced language models when thoughtfully integrated, can lead to real improvements in how well we can analyze sentiments in text. The implementation of our approach is publicly available at: https://github.com/VishalRepos/DESS.
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。
