Ranking Text Documents Based On Conceptual Difficulty Using Term Embedding And Sequential Discourse Cohesion

We propose a novel framework for determining the conceptual difficulty of a domain-specific text document without using any external lexicon. Conceptual difficulty relates to finding the reading difficulty of domain-specific documents. Previous approaches to tackling domain-specific readability problem have heavily relied upon an external lexicon, which limits the scalability to other domains. Our model can be readily applied in domain-specific vertical search engines to re-rank documents according to their conceptual difficulty. We develop an unsupervised and principled approach for computing a term's conceptual difficulty in the latent space. Our approach also considers transitions between the segments generated in sequence. It performs better than the current state-of-the-art comparative methods.