This paper summarizes our research in the area of semantic tagging at the word and sense levels and sets the ground for a new approach to text-level sentiment annotation using a c...
When the training instances of the target class are heavily outnumbered by non-target training instances, SVMs can be ineffective in determining the class boundary. To remedy this...
We describe an adaptation and application of a search-based structured prediction algorithm "Searn" to unsupervised learning problems. We show that it is possible to red...
Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, y...
We present a family of adaptive pairwise tournaments that are provably robust against large error fractions when used to determine the largest element in a set. The tournaments use...
Alina Beygelzimer, John Langford, Pradeep Ravikuma...