Commonsense knowledge-aided news analysis for stock market surveillance
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 | Proceedings of 20th Annual Workshop on Information Technologies and Systems |
| Publisher | Social Science Research Network |
| Publication status | Published - Dec 2010 |
Conference
| Title | 20th Annual Workshop on Information Technologies and Systems, WITS 2010 |
|---|---|
| Place | United States |
| City | St. Louis, MO |
| Period | 11 - 12 December 2010 |
Link(s)
Abstract
Stock market surveillance is critical to maintain market fairness and uphold investors' confidence. This research takes a text mining approach to inspect news to help surveillance specialists investigate suspicious stock transactions. Noticing the important role of prior knowledge in humans' news comprehension, we propose to incorporate commonsense knowledge into this task through a graph model. Experiments on a dataset collected from the Hong Kong stock market show that commonsense knowledge, especially features extracted from inter-news commonsense relations, can significantly improve market surveillance performance.
Citation Format(s)
Commonsense knowledge-aided news analysis for stock market surveillance. / Li, Xin; Chen, Kun; Fung, Terrance et al.
Proceedings of 20th Annual Workshop on Information Technologies and Systems. Social Science Research Network, 2010.
Proceedings of 20th Annual Workshop on Information Technologies and Systems. Social Science Research Network, 2010.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
