Neural sentence embedding models for semantic similarity estimation in the biomedical domain.

ABSTRACT:

Background

Neural network based embedding models are receiving significant attention in the field of natural language processing due to their capability to effectively capture semantic information representing words, sentences or even larger text elements in low-dimensional vector space. While current state-of-the-art models for assessing the semantic similarity of textual statements from biomedical publications depend on the availability of laboriously curated ontologies, unsupervised neural embedding models only require large text corpora as input and do not need manual curation. In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different n

SUBMITTER: Blagec K 

PROVIDER: S-EPMC6460644 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature