We report research toward a never-ending language learning system, focusing on a first implementation which learns to classify occurrences of noun phrases according to lexical categories such as “city” and “university.” Our experiments suggest that the accuracy of classifiers produced by semi-supervised learning can be improved by coupling the learning of multiple classes based on background kn...

Abstract: Resistance testing is an important tool in today’s anti-HIV therapy management for improving the success of antiretroviral therapy. Routinely, the genetic sequence of viral target proteins is obtained. These sequences are then inspected for mutations that might confer resistance to antiretroviral drugs. However, interpretation of the genomic data is challenging. In recent years, appro...

This paper presents a semi-supervised machine-learning approach to predicting whether students will be successful in solving problem-solving tasks within narrative-centered learning environments. Results suggest the approach often outperforms standard supervised learning methods.

We show that for linear classifiers defined by convex marginbased surrogate losses that are monotonically decreasing, it is impossible to construct any semi-supervised approach that is able to guarantee an improvement over the supervised classifier measured by this surrogate loss. For non-monotonically decreasing loss functions, we demonstrate safe improvements are possible.

In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficien...

Semi-supervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semi–supervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semi-supervised approaches in the ...

We propose a general approach to modeling semi-supervised learning constraints on unlabeled data. Both traditional supervised classification tasks and many natural semisupervised learning heuristics can be approximated by specifying the desired outcome of walks through a graph of classifiers. We demonstrate the modeling capability of this approach in the task of relation extraction, and experim...