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People with low vision are one of the primary demographics that make use of accessible content. These users often face difficulties when trying to use regular publications, and the wide variety of vision impairments means the issue of adapting printed publications for this category of readers is far from solved. In this study, we develop a formalized model of adapting textbooks with the help of a semantic network. We then create two multi-level models of factors that influence the quality of adapted textbooks – one with the help of hierarchical modelling, and the other by ranking and determining predicate weights. These models can serve as a baseline for further research into designing a fully optimized model of textbook adaptation.
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Stored only when both papers are in the corpus — 1 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.