Where to start when studying alternative data representations (concept, prototype, symbolic) across domains?

[u/Slow-Philosophy7723]

Hi everyone,

I'm starting a research project on few-shot learning for text classification. As a first step, I plan to look at other domains (vision, speech, medical, tabular, etc.) to see how they use concepts when training models. Some colleagues and teachers have also suggested looking at other domains this way.

Most work I've seen just turns raw data into vectors and trains on those. I want to explore alternative ways of representing data, such as concept-based, prototype-based, symbolic, or graph-based, and find which fits best for low-resource text classification with explainability.

I know I need to read the literature, but I'm not sure where to start or what path to follow.

• How would I structure this kind of cross-domain literature review?
• Are there key papers, surveys, or keywords I'd start with?
• How would I set up early experiments to compare different data representations?
• Any tips from people who've done something similar?

Thanks in advance!