Past and future uses of text mining in ecology and evolution.
Proc Biol Sci
; 289(1975): 20212721, 2022 05 25.
Article
en En
| MEDLINE
| ID: mdl-35582795
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Lenguaje Natural
/
Minería de Datos
Tipo de estudio:
Prognostic_studies
Aspecto:
Ethics
Idioma:
En
Revista:
Proc Biol Sci
Asunto de la revista:
BIOLOGIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Canadá
Pais de publicación:
Reino Unido