A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries.

ABSTRACT: Natural language processing techniques enable extraction of valuable information from large amounts of published literature for the application of data science and technology, i.e. machine learning in the field of materials science. Nevertheless, the automated extraction of data from full-text documents remains a complex task. We propose a document-level natural language processing pipeline for literature extraction of comprehensive information on layered cathode materials for sodium-ion batteries. The pipeline enhances entity recognition with contextual supplementary information while capturing the article structure. Finally, a heuristic multi-level relationship extraction algorithm is employed in relation extraction to extract experimental parameters and complex performance relationships

SUBMITTER: Gou Y 

PROVIDER: S-EPMC11009284 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature