Name
Abstract | ||
|---|---|---|
Developing training data for predicting the relevance of research articles to scientific concepts is a resource-intensive process, and existing datasets are only available for limited subject domains. In this work, we investigate the possibility of weakly supervised data generation for developing relevance models. We approach this by generating document, query, and label triples in an automated ma... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/JCDL52503.2021.00060 | 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) |
Keywords | DocType | ISSN |
Training,Open Access,Biological system modeling,Training data,Tagging,Data models,Libraries | Conference | 2575-7865 |
ISBN | Citations | PageRank |
978-1-6654-1770-9 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Drahomira Herrmannova | 1 | 0 | 0.34 |
| Chathika Gunaratne | 2 | 0 | 0.34 |
| Vickie Walker | 3 | 0 | 0.34 |
| Andrew Rooney | 4 | 0 | 0.34 |
| Robert Patton | 5 | 1 | 1.37 |
| Mary Wolfe | 6 | 0 | 0.34 |
| Charles Schmitt | 7 | 0 | 0.34 |