融合语法、语义与知识多视角信息，提升情感分析准确性
CMV-Fuse: Cross Modal-View Fusion of AMR, Syntax, and Knowledge Representations for Aspect Based Sentiment Analysis
- 通过四种语言视角融合：抽象意义表示、句法解析、依存语法和语义注意力
- 在多个基准上超越强基线，显著提升情感分析性能
- 适合需要高精度情感理解的NLP应用，如舆情分析与智能客服
自然语言理解依赖于从表层句法到深层语义及世界知识等多个互补视角的整合。然而，当前方面级情感分析（ABSA）系统通常仅利用孤立的语言视角，忽视了人类天然具备的结构化表示间复杂互动。我们提出CMV-Fuse，一种跨模态视角融合框架，通过系统结合四种语言视角——抽象意义表示、成分句法、依存句法与语义注意力，并引入外部知识增强。采用分层门控注意力机制，在局部句法、中间语义与全局知识层面实现融合，捕捉细粒度结构模式与整体上下文理解。创新性地设计结构感知多视角对比学习机制，确保互补表示一致性的同时保持计算效率。大量实验表明，该方法在标准基准上显著优于强基线，分析揭示各语言视角对情感分析鲁棒性的贡献。
原文摘要 · Abstract (English)
Natural language understanding inherently depends on integrating multiple complementary perspectives spanning from surface syntax to deep semantics and world knowledge. However, current Aspect-Based Sentiment Analysis (ABSA) systems typically exploit isolated linguistic views, thereby overlooking the intricate interplay between structural representations that humans naturally leverage. We propose CMV-Fuse, a Cross-Modal View fusion framework that emulates human language processing by systematically combining multiple linguistic perspectives. Our approach systematically orchestrates four linguistic perspectives: Abstract Meaning Representations, constituency parsing, dependency syntax, and semantic attention, enhanced with external knowledge integration. Through hierarchical gated attention fusion across local syntactic, intermediate semantic, and global knowledge levels, CMV-Fuse captures both fine-grained structural patterns and broad contextual understanding. A novel structure aware multi-view contrastive learning mechanism ensures consistency across complementary representations while maintaining computational efficiency. Extensive experiments demonstrate substantial improvements over strong baselines on standard benchmarks, with analysis revealing how each linguistic view contributes to more robust sentiment analysis.
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