Learning To Rank Relevant Documents for Information Retrieval in Bioengineering Text Corpora

In this paper, we present a Learning To Rank-based approach that helps EXPLORE and understand Relevant documents for bioengineering text corpora, called LTREXPLORER. Based on the likelihood of being the most relevance to a search query, the ranking model sorts documents according to their degrees of relevance, preference, or importance with various domain-specific features. The evaluation results demonstrated that our approach has the potential to effectively provide the retrieval scoring functions to researchers, who focus on the most relevant documents in bioengineering information retrieval.