AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The Syntactic-optimal Transport Graph Network (SOT-Graph) is proposed, a model that jointly integrates structural and distributional signals and outperforms existing baselines by a margin of 1.30% Macro-F1 on Laptop14 and 1.01% on Twitter.
A graph fusion network that leverages augmented semantic and syntactic graphs that explicitly model word‐dependency correlations via contextual augmentation, and incorporate selected part‐of‐speech (POS) features to refine semantic graph construction is proposed.
A sentiment-aware graph anomaly detection framework that integrates graph anomaly detection with a sentiment consistency evaluation mechanism to eliminate misleading dependencies and preserve key sentiment information from the original graph and can be seamlessly incorporated into other graph-based ABSA models to enhan...
The proposed framework addresses several limitations of existing graph-based ABSA systems, including static lexicon weighting, parser uncertainty, and lack of multi-scale linguistic representations.
This work introduces an Enhanced Affinity Fusion module that explicitly strengthens aspect–opinion relational modeling by selectively integrating complementary attention mechanisms, and proposes Label-Guided Data Amplification (LGDA), which enhances supervision diversity and domain robustness through label-driven text...
The results demonstrate that incorporating explicit syntactic knowledge, adaptive feature fusion, and structured decoding substantially improves cross-domain generalization in fine-grained sentiment analysis.
A Knowledge-Enhanced and Multidimensional Feature Fusion (KEMFF) model for ABSA, which captures sentiment feature representations across multiple dimensions, including syntax, semantics, and knowledge, and aligns them into a unified aspect-level representation.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.