Manual indexing is slow and expensive as finding the relevant indexing terms is a time-consuming task and in the case of full-text of articles, it could be overwhelming. On the other hand, automated document indexing is faster, more reliable, and cost-effective compared to manual indexing.
Straive offers an automated deep indexing solution that leverages cutting-edge analytics and data science methodologies to classify even unstructured documents using the extreme multilabel classification (XMLC) method.
Deep indexing by extreme multilabel classification is a highly efficient methodology for indexing:
Download our case study to learn how Straive used the extreme multilabel classification (XMLC) model for extracting the top 25 terms as per matching score with thesaurus from an extensive biomedical database.