New research explores understanding scientific formulae in information retrieval

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

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →