Learning to identify TV news monologues by style and context
We focus on the problem of learning semantics from multimedia data associated with broadcast video documents. In this paper we propose to learn semantic concepts from multimodal sources based on style and context detectors, in combination with statistical classier ensembles. As a case study we present our method for detecting the concept of news subject monologues. This approach had the best average precision performance amongst 26 submissions in the 2003 video track of the Text Retrieval Conference benchmark. Experiments were conducted with respect to individual detector contribution, ensemble size, and ranking mechanism. It was found that the combination of detectors is decisive for the nal result, although some detectors might appear useless in isolation. Moreover, by using a probabilistic ranking, in combination with a large classier ensemble, results can be improved even further.
Acknowledments Judgment Day: A Vignette Introduction: The Supreme Court Under the Media Lens 1 A Year in the Life of the Supreme Court 2 Equal in Alberta: The Vriend Case 3 Court and Spin Country: The Quebec Secession Reference 4 "Sea of Confusion": R. v. Marshall 5 "Parents Can Sleep Soundly": The Queen v. John Robin Sharpe 6 Judges and Journalists Conclusion: Reporting the Supreme Court through a Political Prism Appendix A: Interview Questions Appendix B: Method of Analysis -- Coding Instructions and Sample Code Sheet About the Authors Index