We present our hybrid system for the PAN challenge at CLEF 2010. Our system performs plagiarism detection for translated and non-translated externally as well as intrinsically plag...
Markus Muhr, Roman Kern, Mario Zechner, Michael Gr...
We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
Traditional boosting algorithms for the ranking problems usually employ the pairwise approach and convert the document rating preference into a binary-value label, like RankBoost....
Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang...
Information retrieval over semantic metadata has recently received a great amount of interest in both industry and academia. In particular, discovering complex and meaningful rela...
Christian Halaschek-Wiener, Boanerges Aleman-Meza,...
We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online learning algorithms. The a...