𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗟𝗶𝘁𝗲𝗿𝗮𝘁𝘂𝗿𝗲 𝗥𝗲𝘃𝗶𝗲𝘄

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗟𝗶𝘁𝗲𝗿𝗮𝘁𝘂𝗿𝗲 𝗥𝗲𝘃𝗶𝗲𝘄

Searching for papers takes too long. Manual searches miss key work. You need a system.

Use synonym rings. Break your research question into blocks. List synonyms and acronyms in a spreadsheet. This forms your search query. You find papers regardless of the jargon.

Example: You study medical image segmentation. You group terms like few-shot and n-shot. You find papers you would miss with a simple search.

The Semantic Scholar API helps. It uses vector similarity to find related papers.

Follow these steps:

• Create synonym rings for each research block.
• Pull metadata and TLDR summaries.
• Filter by top journals and citation counts.

Start small. Test your pipeline on one year of data. Add missing terms to your rings. Scale up once you verify results.

This process makes your review reproducible. You get high recall without noise.

Source: https://dev.to/ken_deng_ai/automating-literature-review-synthesis-from-search-strings-to-a-curated-paper-corpus-4a25 Optional learning community: https://t.me/GyaanSetuAi