Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...
In this paper we investigate a novel and important problem in multi-document summarization, i.e., how to extract an easy-tounderstand English summary for non-native readers. Exist...
After two successful years at SIGIR in 2007 and 2008, the third workshop on Searching Spontaneous Conversational Speech (SSCS 2009) was held conjunction with the ACM Multimedia 20...
Martha Larson, Roeland Ordelman, Franciska de Jong...
In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the l...
This paper explores correspondence and mixture topic modeling of documents tagged from two different perspectives. There has been ongoing work in topic modeling of documents with...