Version 1
: Received: 23 August 2024 / Approved: 26 August 2024 / Online: 26 August 2024 (10:23:08 CEST)
How to cite:
Galli, C.; Cusano, C.; Guizzardi, S.; Donos, N.; Calciolari, E. Embeddings in Literature Screening: A Gentle Introduction to Vectorial Semantics for Life Science Investigators. Preprints2024, 2024081823. https://doi.org/10.20944/preprints202408.1823.v1
Galli, C.; Cusano, C.; Guizzardi, S.; Donos, N.; Calciolari, E. Embeddings in Literature Screening: A Gentle Introduction to Vectorial Semantics for Life Science Investigators. Preprints 2024, 2024081823. https://doi.org/10.20944/preprints202408.1823.v1
Galli, C.; Cusano, C.; Guizzardi, S.; Donos, N.; Calciolari, E. Embeddings in Literature Screening: A Gentle Introduction to Vectorial Semantics for Life Science Investigators. Preprints2024, 2024081823. https://doi.org/10.20944/preprints202408.1823.v1
APA Style
Galli, C., Cusano, C., Guizzardi, S., Donos, N., & Calciolari, E. (2024). Embeddings in Literature Screening: A Gentle Introduction to Vectorial Semantics for Life Science Investigators. Preprints. https://doi.org/10.20944/preprints202408.1823.v1
Chicago/Turabian Style
Galli, C., Nikolaos Donos and Elena Calciolari. 2024 "Embeddings in Literature Screening: A Gentle Introduction to Vectorial Semantics for Life Science Investigators" Preprints. https://doi.org/10.20944/preprints202408.1823.v1
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 a knowledge of how meaning representation strategies work is crucial to make 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.
Keywords
embeddings; life sciences; academic publications
Subject
Biology and Life Sciences, Life Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
