Citation-Driven Multi-View Training for Patent Embeddings: QaECTER and Sophia-Bench
Learn how to improve patent retrieval with citation-driven multi-view training for patent embeddings using QaECTER and Sophia-Bench, and apply this knowledge to enhance innovation and IP strategy decisions
- Apply citation-driven multi-view training to patent embeddings using QaECTER
- Evaluate the performance of patent retrieval models using Sophia-Bench
- Configure patent retrieval benchmarks to reflect real-world search scenarios
- Test the effectiveness of QaECTER and Sophia-Bench in improving patent retrieval accuracy
- Compare the results of citation-driven multi-view training with traditional training methods
Data scientists and machine learning engineers working on patent retrieval and analysis can benefit from this research to improve the accuracy of their models and enhance decision-making in innovation and IP strategy
💡 Citation-driven multi-view training can significantly improve patent retrieval accuracy by capturing the diversity of real-world search scenarios
Boost patent retrieval accuracy with QaECTER and Sophia-Bench! #patentretrieval #machinelearning
Key Takeaways
Learn how to improve patent retrieval with citation-driven multi-view training for patent embeddings using QaECTER and Sophia-Bench, and apply this knowledge to enhance innovation and IP strategy decisions
Full Article
Abstract:
arXiv:2604.22897v1 Announce Type: cross Abstract: Patent retrieval underpins critical decisions in innovation, examination, and IP strategy, yet progress has been hampered by the absence of benchmarks that reflect the diversity of real world search scenarios. We address this gap with two contributions. First, we introduce Sophiabench, a large-scale patent retrieval benchmark comprising 10,000 queries and 75,000 corpus documents stratified across ten years, eight IPC technology sections, and twel
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