Datasets:
Tasks:
Text Classification
Formats:
csv
Sub-tasks:
multi-label-classification
Languages:
English
Size:
10K - 100K
License:
File size: 960 Bytes
9b03c35 94106c3 9b03c35 94106c3 9f44f85 94106c3 9f44f85 50e25ed 9b03c35 94106c3 50e25ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
---
language:
- en
license: afl-3.0
source_datasets:
- BioASQ Task A
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: BioASQ, PUBMED
size_categories:
- 10K<n<100K
---
This dataset consists of a approx 50k collection of research articles from **PubMed** repository. Originally these documents are manually annotated by Biomedical Experts with their MeSH labels and each articles are described in terms of 10-15 MeSH labels. In this Dataset we have huge numbers of labels present as a MeSH major which is raising the issue of extremely large output space and severe label sparsity issues. To solve this Issue Dataset has been Processed and mapped to its root as Described in the Below Figure.
 |