Embeddings for Efficient Literature Screening: A Primer for Life Science Investigators

Abstract

As the number of publications is quickly growing in any area of science, the need to efficiently find relevant information amidst a large number of similarly themed articles becomes very important. Semantic searching through text documents has the potential to overcome the limits of keyword-based searches, especially since the introduction of attention-based transformers, which can capture contextual nuances of meaning in single words, sentences, or whole documents. The deployment of these computational tools has been made simpler and accessible to investigators in every field of research thanks to a growing number of dedicated libraries, but knowledge of how meaning representation strategies work is crucial to making the most out of these instruments. The present work aims at introducing the technical evolution of the meaning representation systems, from vectors to embeddings and transformers tailored to life science investigators with no previous knowledge of natural language processing.