Researchers have explored the challenge of understanding scientific formulae in scholarly information retrieval, noting their dual nature as structured syntax and semantic carriers. A study found that while formulae exhibit strong latent correlation between syntax and semantics, their native representation spaces show very weak observable correspondence, indicating a significant mismatch. By employing graph-based encoders for syntax and text-based encoders for semantics, and applying contrastive learning to create a shared representation space, the researchers demonstrated that explicit representation learning can substantially improve cross-modal retrieval. AI
IMPACT This research could improve how scientific literature is searched and understood, potentially aiding researchers in discovering relevant papers and formulas.
RANK_REASON The item is an academic paper submitted to arXiv detailing research on information retrieval techniques for scientific formulae. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.IR (Information Retrieval) →
- alphaXiv
- arXiv
- arXivLabs
- CatalyzeX Code Finder for Papers
- Connected Papers
- contrastive learning
- CORE Recommender
- DagsHub
- Gotit.pub
- graph-based encoders
- Hugging Face
- Influence Flower
- Litmaps
- ScienceCast
- scite Smart Citations
- Syntax Meets Semantics: Understanding Scientific Formulae
- text-based encoders
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
