List-wise learning to rank biomedical question-answer pairs with deep ranking recursive autoencoders.

ABSTRACT: Biomedical question answering (QA) represents a growing concern among industry and academia due to the crucial impact of biomedical information. When mapping and ranking candidate snippet answers within relevant literature, current QA systems typically refer to information retrieval (IR) techniques: specifically, query processing approaches and ranking models. However, these IR-based approaches are insufficient to consider both syntactic and semantic relatedness and thus cannot formulate accurate natural language answers. Recently, deep learning approaches have become well-known for learning optimal semantic feature representations in natural language processing tasks. In this paper, we present a deep ranking recursive autoencoders (rankingRAE) architecture for ranking question-candidate s

SUBMITTER: Yan Y 

PROVIDER: S-EPMC7652278 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature