Learning to rank only using training data from related domain
Like traditional supervised and semi-supervised algorithms, learning to rank for information retrieval requires document annotations provided by domain experts. It is costly to annotate training data for different search domains and tasks. We propose to exploit training data annotated for a related...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2010
|
| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/4597 https://ink.library.smu.edu.sg/context/sis_research/article/5600/viewcontent/p162_gao.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Singapore Management University |
| Language: | English |