Domain-Specific Iterative Readability Computation

The authors present a new algorithm to measure domain-specific readability. It iteratively computes the readability of domain-specific resources based on the difficulty of domain-specific concepts and vice versa, in a style reminiscent of other bi-partite graph algorithms such as Hyperlink-Induced Topic Search (HITS) and the Stochastic Approach for Link-Structure Analysis (SALSA). While simple, the algorithm outperforms standard heuristic measures and remains competitive among supervised-learning approaches. Moreover, it is less domain-dependent and portable across domains as it does not rely on an annotated corpus or expensive expert knowledge that supervised or domain-specific methods require.