Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning

arXiv:2605.10073 · cs.CL · Submitted 2026-05-11 · Read on arXiv

cs.CL

Submitted: 2026-05-11

Updated: 2026-08-31

Terminology

• Clustering — 9× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Graph Construction — 7× in this paper · explained in 3 episodes
• Representation Learning — 7× in this paper · explained in 2 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Claim Dependency Graph — 6× in this paper
• Transformer — 6× in this paper · explained in 2 episodes
• Attention Mechanism — 5× in this paper · explained in 2 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Homogeneity — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Guided attention — 2× in this paper · explained in 2 episodes
• Heterogeneous Graph — 2× in this paper · explained in 2 episodes
• Patent Heterogeneous Attention Graph Encoder — 2× in this paper
• Sparsity — 2× in this paper · explained in 4 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• International Patent Classification — 1× in this paper
• Linear Probing — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Masked Language Model — 1× in this paper
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Linguistically Informed Masking for Representation Learning in the Patent Domain
• PatenTEB: A Comprehensive Benchmark and Model Family for Patent Text Embedding
• PaECTER: Patent-level Representation Learning using Citation-informed Transformers
• Research on feature fusion and multimodal patent text based on graph attention network
• PatentMind: A Multi-Aspect Reasoning Graph for Patent Similarity Evaluation
• Patent Language Model Pretraining with ModernBERT
• Patent Representation Learning via Self-supervision

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering