Abstract. In many supervised learning tasks it can be costly or infeasible to obtain objective, reliable labels. We may, however, be able to obtain a large number of subjective, po...
People are seldom aware that their search queries frequently mismatch a majority of the relevant documents. This may not be a big problem for topics with a large and diverse set o...
Abstract. This paper describes an efficient method to construct reliable machine learning applications in peer-to-peer (P2P) networks by building ensemble based meta methods. We co...
For automatic semantic annotation of large-scale video database, the insufficiency of labeled training samples is a major obstacle. General semi-supervised learning algorithms can...
This paper presents DEPEVAL(summ), a dependency-based metric for automatic evaluation of summaries. Using a reranking parser and a Lexical-Functional Grammar (LFG) annotation, we ...