We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
This paper studies the problem of learning from ambiguous supervision, focusing on the task of learning semantic correspondences. A learning problem is said to be ambiguously supe...
While Active Learning (AL) has already been shown to markedly reduce the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned a...
We consider the situation in semi-supervised learning, where the "label sampling" mechanism stochastically depends on the true response (as well as potentially on the fe...
Abstract. When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional ...