Learning features through feedback for blog distillation
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Pages | 1085-1086 |
| Publication status | Published - 2011 |
Conference
| Title | 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11 |
|---|---|
| Place | China |
| City | Beijing |
| Period | 24 - 28 July 2011 |
Link(s)
Abstract
The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.
Research Area(s)
- Blog distillation, Faceted distillation, Feedback
Citation Format(s)
Learning features through feedback for blog distillation. / Gao, Dehong; Zhang, Renxian; Li, Wenjie et al.
SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011. p. 1085-1086.
SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011. p. 1085-1086.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
