CMV-Fuse: Cross Modal-View Fusion of AMR, Syntax, and Knowledge Representations for Aspect Based Sentiment Analysis

• URL: http://arxiv.org/abs/2512.06679v1
• Date: Sun, 07 Dec 2025 06:35:46 GMT
• Title: CMV-Fuse: Cross Modal-View Fusion of AMR, Syntax, and Knowledge Representations for Aspect Based Sentiment Analysis
• Authors: Smitha Muthya Sudheendra, Mani Deep Cherukuri, Jaideep Srivastava,
• Abstract summary: CMV-Fuse is a Cross-Modal View fusion framework that emulates human language processing.<n>Our approach systematically orchestrates four linguistic perspectives.<n> CMV-Fuse captures both fine-grained structural patterns and broad contextual understanding.
• Score: 2.2972913486393707
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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