Past and future uses of text mining in ecology and evolution

Farrell, Maxwell J, Brierley, Liam ORCID: 0000-0002-3026-4723, Willoughby, Anna, Yates, Andrew and Mideo, Nicole (2022) Past and future uses of text mining in ecology and evolution. PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 289 (1975). 20212721-.

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Official URL: http://dx.doi.org/10.1098/rspb.2021.2721

Abstract

Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues.

Item Type:	Article
Uncontrolled Keywords:	natural language processing, literature synthesis, computational linguistics, information extraction, database construction
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health
Depositing User:	Symplectic Admin
Date Deposited:	23 May 2022 15:09
Last Modified:	18 Jan 2023 21:01
DOI:	10.1098/rspb.2021.2721
Open Access URL:	https://royalsocietypublishing.org/doi/10.1098/rsp...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3155344