A sequential latent topic-based readability model for domain-specific information retrieval

In domain-specific information retrieval (IR), an emerging problem is how to provide different users with documents that are both relevant and readable, especially for the lay users. In this paper, we propose a novel document readability model to enhance the domain-specific IR. Our model incorporates the coverage and sequential dependency of latent topics in a document. Accordingly, two topical readability indicators, namely Topic Scope and Topic Trace are developed. These indicators, combined with the classical Surface-level indicator, can be used to rerank the initial list of documents returned by a conventional search engine. In order to extract the structured latent topics without supervision, the hierarchical Latent Dirichlet Allocation (hLDA) is used. We have evaluated our model from the user-oriented and system-oriented perspectives, in the medical domain. The user-oriented evaluation shows a good correlation between the readability scores given by our model and human judgments. Furthermore, our model also gains significant improvement in the system-oriented evaluation in comparison with one of the state-of-the-art readability methods.