This paper describes a new approach to predicting the aspectual class of verbs in context, i.e., whether a verb is used in a stative or dynamic sense. We identify two challenging cases of this problem: when the verb is unseen in training data, and when the verb is ambiguous for aspectual class. A semi-supervised approach using linguistically-motivated features and a novel set of distributional ...

Syndromic Surveillance has been performed using machine learning and other statistical methods to detect disease outbreaks. These methods are largely dependent on the availability of historical data to train the machine learning-based surveillance system. However, relevant training data may differ from region to region due to geographical and seasonal trends, meaning that the syndromic surveill...

The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled in order to capture variations in object appearance. Semi-supervised training is a means for reducing the effort needed to prepare the training set by training the model with a small number of fully labeled examples and ...

We consider the problem of learning a classifier when we dispose little training data from the target domain but abundant training data from several source domains. We make two contributions to the domain adaptation problem. First we extend the Nearest Class Mean (NCM) classifier by introducing for each class domain-dependent mean parameters as well as domain-specific weights. Second, we propos...

Semi-Circular Bending (SCB) Test is a fast and accurate three-point bending test, which was originally used in rock mechanics. SCB test is going to be an accepted test method for asphalt concrete pavements. Different asphalt-mixture property-values such as tensile strength, stress intensity factor and fatigue can be obtained by this test. In this study, static and dynamic tests including SCB te...

Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled da...