%0 Conference Proceedings
%9 ACTI : Communications avec actes dans un congrès international
%A Sahraoui, M.
%A Sklab, Youcef
%A Pignal, M.
%A Vignes Lebbe, R.
%A Guigue, V.
%T Leveraging multimodality for biodiversity data : exploring joint representations of species descriptions and specimen images using CLIP
%D 2023
%L fdi:010091411
%G ENG
%J Biodiversity Information Science and Standards
%@ 2535-0897
%P e112666 [4 ]
%R 10.3897/biss.7.112666
%U https://www.documentation.ird.fr/hor/fdi:010091411
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2024-08/010091411.pdf
%V 7
%W Horizon (IRD)
%X In recent years, the field of biodiversity data analysis has witnessed significant advancements, with a number of models emerging to process and extract valuable insights from various data sources. One notable area of progress lies in the analysis of species descriptions, where structured knowledge extraction techniques have gained prominence. These techniques aim to automatically extract relevant information from unstructured text, such as taxonomic classifications and morphological traits. (Sahraoui et al. 2022, Sahraoui et al. 2023) By applying natural language processing (NLP) and machine learning methods, structured knowledge extraction enables the conversion of textual species descriptions into a structured format, facilitating easier integration, searchability, and analysis of biodiversity data.
%$ 122 ; 082